From 1a78e607690184c702c1cb7de535c24b119c6b5e Mon Sep 17 00:00:00 2001 From: Vivek Vardhan <91594529+vivekvardhan2810@users.noreply.github.com> Date: Tue, 16 Jul 2024 14:07:09 +0530 Subject: [PATCH] Add files via upload --- .../Dataset/pet_adoption_data.csv | 2008 +++++++++++++++++ .../Pet_Adoption_Status_Prediction.ipynb | 959 ++++++++ Pet Adoption Status/requirements.txt | 5 + 3 files changed, 2972 insertions(+) create mode 100644 Pet Adoption Status/Dataset/pet_adoption_data.csv create mode 100644 Pet Adoption Status/Model/Pet_Adoption_Status_Prediction.ipynb create mode 100644 Pet Adoption Status/requirements.txt diff --git a/Pet Adoption Status/Dataset/pet_adoption_data.csv b/Pet Adoption Status/Dataset/pet_adoption_data.csv new file mode 100644 index 000000000..8831ce0ee --- /dev/null +++ b/Pet Adoption Status/Dataset/pet_adoption_data.csv @@ -0,0 +1,2008 @@ +PetID,PetType,Breed,AgeMonths,Color,Size,WeightKg,Vaccinated,HealthCondition,TimeInShelterDays,AdoptionFee,PreviousOwner,AdoptionLikelihood +500,Bird,Parakeet,131,Orange,Large,5.039767822529515,1,0,27,140,0,0 +501,Rabbit,Rabbit,73,White,Large,16.086726854616735,0,0,8,235,0,0 +502,Dog,Golden Retriever,136,Orange,Medium,2.0762862789067658,0,0,85,385,0,0 +503,Bird,Parakeet,97,White,Small,3.339423254344144,0,0,61,217,1,0 +504,Rabbit,Rabbit,123,Gray,Large,20.49809976164308,0,0,28,14,1,0 +505,Dog,Labrador,70,Brown,Large,20.986260902397984,0,0,87,301,1,0 +506,Bird,Parakeet,169,Brown,Small,10.90261279164483,1,0,70,440,1,0 +507,Cat,Siamese,13,Orange,Large,7.252683192048298,1,0,3,137,0,1 +508,Bird,Parakeet,49,Brown,Medium,24.59759785196193,1,1,69,405,0,0 +509,Bird,Parakeet,60,Gray,Large,7.2959936546268205,0,0,73,231,1,0 +510,Bird,Parakeet,130,Orange,Large,16.71878502032501,0,0,7,88,0,0 +511,Rabbit,Rabbit,5,White,Small,29.078056385024258,1,0,60,462,0,1 +512,Dog,Golden Retriever,172,Brown,Large,5.28772069800913,1,0,4,76,1,0 +513,Cat,Siamese,27,Black,Large,28.28541175734656,1,0,5,135,0,0 +514,Cat,Persian,160,Brown,Medium,6.303898538991391,1,0,11,404,0,1 +515,Rabbit,Rabbit,149,White,Small,17.45585303267366,1,0,15,468,1,0 +516,Cat,Persian,8,Orange,Small,11.933223646803594,1,1,64,405,1,0 +517,Cat,Persian,50,White,Medium,28.98292944355295,1,0,13,109,0,1 +518,Rabbit,Rabbit,67,Black,Large,8.932718841056172,1,1,7,352,1,0 +519,Dog,Labrador,44,Gray,Small,12.590082060486855,0,1,6,300,0,0 +520,Rabbit,Rabbit,125,Brown,Small,29.502943417807195,1,0,56,90,0,0 +521,Cat,Persian,58,Orange,Large,16.557641344465722,0,0,64,423,1,0 +522,Dog,Labrador,3,Gray,Large,15.57627335301635,1,0,50,369,1,1 +523,Dog,Golden Retriever,86,White,Small,8.475576157915746,1,0,30,405,0,0 +524,Bird,Parakeet,70,Orange,Large,21.3395432490028,1,0,22,413,0,0 +525,Bird,Parakeet,130,Gray,Small,1.4168156015555518,1,0,25,189,0,0 +526,Cat,Persian,172,Orange,Large,2.4358662613665123,1,0,28,72,0,0 +527,Rabbit,Rabbit,2,Black,Large,18.38139902693828,1,1,84,172,0,0 +528,Rabbit,Rabbit,102,Black,Medium,10.90856264073517,0,0,39,34,0,0 +529,Rabbit,Rabbit,35,Brown,Large,28.646174793827857,0,1,67,209,0,0 +530,Bird,Parakeet,98,Brown,Medium,27.192530030960786,1,0,5,297,0,1 +531,Cat,Persian,30,Black,Large,21.69748150162451,1,1,65,132,0,0 +532,Bird,Parakeet,116,Orange,Small,24.72622185270624,1,0,15,282,1,0 +533,Cat,Siamese,8,Black,Small,20.352623640363106,1,0,67,197,0,1 +534,Rabbit,Rabbit,150,Brown,Large,4.64188097859466,1,0,40,445,0,0 +535,Bird,Parakeet,44,Black,Large,23.20075772098688,0,0,69,31,1,0 +536,Rabbit,Rabbit,36,Black,Small,7.095567092846545,1,0,77,135,1,0 +537,Rabbit,Rabbit,77,Orange,Large,10.379842797905706,0,0,43,247,0,0 +538,Dog,Golden Retriever,31,Gray,Medium,24.757792150970186,1,0,1,358,0,1 +539,Dog,Golden Retriever,47,Brown,Medium,16.55924264341,0,0,22,340,1,0 +540,Bird,Parakeet,55,Orange,Medium,25.938280711318868,1,0,62,145,0,1 +541,Dog,Labrador,106,Black,Medium,7.1780242754546615,0,0,79,333,1,1 +542,Bird,Parakeet,158,Black,Medium,18.62557471959973,1,0,26,142,0,1 +543,Cat,Persian,121,Gray,Large,3.429938203899587,1,1,18,233,0,0 +544,Dog,Poodle,55,Brown,Large,19.64876881266086,1,0,48,409,0,0 +545,Cat,Persian,144,Brown,Medium,23.11348709549051,1,0,9,436,1,1 +546,Dog,Poodle,12,White,Large,17.962995925382927,1,1,1,43,1,0 +547,Dog,Poodle,145,Brown,Large,8.720713543465497,1,0,9,31,0,0 +548,Rabbit,Rabbit,144,Gray,Medium,1.9089373969071348,1,0,23,230,0,1 +549,Rabbit,Rabbit,140,White,Large,4.4640176349642875,0,0,45,27,0,0 +550,Bird,Parakeet,136,White,Large,19.04678639314676,1,0,2,422,0,0 +551,Rabbit,Rabbit,3,Black,Large,28.0981862884329,0,0,14,490,1,0 +552,Bird,Parakeet,144,Black,Medium,6.989497715188185,0,0,38,159,0,0 +553,Rabbit,Rabbit,30,Brown,Large,22.315758324047863,1,0,40,332,1,0 +554,Dog,Golden Retriever,23,White,Small,26.127080834222205,1,0,63,206,0,1 +555,Bird,Parakeet,92,Black,Small,16.10558317497782,1,0,82,17,1,0 +556,Cat,Siamese,115,Gray,Medium,20.408708079466074,1,1,16,102,1,0 +557,Rabbit,Rabbit,104,Black,Small,24.635444183608296,0,0,75,69,0,0 +558,Cat,Persian,118,Black,Medium,8.559579574477214,0,1,6,118,0,0 +559,Rabbit,Rabbit,5,White,Small,9.609974553773247,1,1,3,138,0,0 +560,Cat,Persian,104,White,Small,1.018197982225229,1,0,14,159,0,0 +561,Cat,Persian,108,Orange,Small,28.929729796036273,0,0,66,231,0,0 +562,Cat,Persian,65,Gray,Medium,27.608801943294,1,0,75,147,0,1 +563,Cat,Siamese,121,White,Medium,23.17247964902348,1,1,86,352,1,0 +564,Bird,Parakeet,30,Brown,Large,3.2932046083227955,1,0,67,423,0,0 +565,Cat,Persian,149,Gray,Medium,13.977583801285736,1,1,31,391,1,0 +566,Rabbit,Rabbit,84,Gray,Small,3.9548608651750317,1,0,33,242,1,0 +567,Cat,Persian,150,White,Small,1.4616877506211594,1,0,88,34,1,0 +568,Cat,Persian,32,Gray,Medium,18.869305991165323,0,0,1,324,0,0 +569,Cat,Siamese,161,White,Medium,28.864053095325463,1,0,44,381,0,1 +570,Rabbit,Rabbit,16,Black,Medium,27.183172679532355,1,0,15,354,0,1 +571,Bird,Parakeet,38,Orange,Large,21.205889633126727,0,0,37,238,0,0 +572,Rabbit,Rabbit,142,Black,Small,3.8025769407812713,1,0,27,333,0,0 +573,Cat,Siamese,36,White,Large,8.950757092496705,1,0,48,148,1,0 +574,Rabbit,Rabbit,151,White,Large,10.281645079673439,1,1,27,110,0,0 +575,Dog,Poodle,114,Black,Small,23.131351674621275,0,1,12,407,0,0 +576,Rabbit,Rabbit,66,Brown,Small,24.265226197818308,1,0,67,16,1,0 +577,Dog,Labrador,105,Brown,Medium,12.168077566707693,1,0,30,401,0,1 +578,Cat,Siamese,85,Brown,Medium,13.11863590191068,0,0,87,225,0,0 +579,Dog,Poodle,2,White,Large,2.2907621390089687,1,0,66,139,1,1 +580,Dog,Labrador,75,Gray,Medium,9.384964834144068,1,0,7,143,0,1 +581,Dog,Labrador,67,White,Medium,14.087270138151558,1,0,34,259,0,1 +582,Bird,Parakeet,151,Gray,Small,4.39250834517247,0,1,32,216,0,0 +583,Dog,Labrador,154,Black,Small,14.144756361895618,1,1,39,161,1,0 +584,Dog,Golden Retriever,55,Orange,Medium,25.74209855547621,1,0,43,91,0,1 +585,Dog,Labrador,6,Gray,Small,17.858309572026545,1,0,43,173,0,1 +586,Rabbit,Rabbit,163,Brown,Medium,3.288965796113957,1,0,78,330,0,1 +587,Rabbit,Rabbit,150,White,Small,6.558468246077956,1,0,3,204,0,0 +588,Rabbit,Rabbit,171,Black,Small,14.76178615459813,0,0,46,414,0,0 +589,Bird,Parakeet,143,Brown,Small,11.48312734483255,1,0,32,152,0,0 +590,Bird,Parakeet,86,Black,Small,11.403083566836864,1,0,43,102,0,0 +591,Bird,Parakeet,60,Orange,Medium,16.348755996164115,1,1,81,201,0,0 +592,Dog,Golden Retriever,101,White,Large,17.947481726094264,0,0,3,279,0,0 +593,Bird,Parakeet,12,White,Small,24.10898553800954,1,1,74,64,0,0 +594,Dog,Golden Retriever,49,Orange,Medium,10.888030340412055,1,1,63,116,0,0 +595,Dog,Poodle,42,White,Medium,24.358522208177018,1,1,40,152,0,0 +596,Bird,Parakeet,166,Gray,Small,10.819725644266343,0,0,18,155,0,0 +597,Cat,Siamese,82,Black,Large,2.607556679295781,0,0,67,163,1,0 +598,Rabbit,Rabbit,173,Brown,Small,17.071441879138945,0,0,62,81,1,0 +599,Bird,Parakeet,35,Orange,Medium,18.049167421949623,1,0,10,5,0,1 +600,Dog,Poodle,158,Gray,Large,28.101667123033945,0,0,52,321,0,0 +601,Dog,Golden Retriever,158,Brown,Medium,22.055778455811758,0,0,73,66,0,0 +602,Bird,Parakeet,134,Brown,Small,27.615406922338746,0,0,46,237,0,0 +603,Cat,Persian,67,Orange,Large,27.34206909704728,1,0,55,71,0,0 +604,Dog,Golden Retriever,80,Gray,Medium,18.53264919748902,0,1,46,94,0,0 +605,Rabbit,Rabbit,7,Brown,Medium,16.305099894756978,1,0,75,303,1,1 +606,Rabbit,Rabbit,14,Brown,Medium,17.165788047744396,0,0,60,466,0,1 +607,Cat,Siamese,73,Gray,Small,29.371593422640185,1,0,26,497,0,0 +608,Bird,Parakeet,143,White,Large,23.421863060661806,1,0,27,325,1,0 +609,Dog,Golden Retriever,149,Brown,Small,4.081711238979479,1,0,38,77,0,0 +610,Dog,Golden Retriever,163,Black,Small,23.574423796660597,1,0,22,66,0,0 +611,Cat,Siamese,66,Gray,Small,5.945603418048551,1,0,26,175,1,0 +612,Dog,Labrador,91,Brown,Large,13.973165921163144,1,0,86,320,1,1 +613,Cat,Siamese,130,Orange,Small,7.103330493119494,1,0,69,171,1,0 +614,Bird,Parakeet,163,Black,Small,20.997908900463663,1,0,35,477,0,0 +615,Cat,Siamese,20,Brown,Small,26.441982349598035,1,0,66,254,0,1 +616,Bird,Parakeet,10,Black,Small,9.420494675633748,1,0,73,62,1,1 +617,Dog,Golden Retriever,25,Black,Medium,3.3783114950374413,1,0,51,301,0,1 +618,Bird,Parakeet,25,White,Small,9.656299844173935,1,1,18,402,1,0 +619,Cat,Persian,54,Black,Medium,20.49849388003364,1,1,43,245,0,0 +620,Rabbit,Rabbit,33,White,Small,10.534043699079048,0,0,12,191,0,0 +621,Dog,Golden Retriever,164,Gray,Large,9.0652528590603,0,0,3,466,1,0 +622,Bird,Parakeet,95,White,Medium,6.348620526943684,1,0,82,328,0,1 +623,Rabbit,Rabbit,14,Orange,Medium,15.427976379358572,0,0,7,70,0,1 +624,Bird,Parakeet,65,Brown,Medium,9.00251601655844,1,0,6,365,0,1 +625,Dog,Labrador,110,Gray,Medium,22.19930012504501,0,0,82,228,0,1 +626,Rabbit,Rabbit,80,Gray,Large,3.2220496577738786,1,0,45,433,0,0 +627,Rabbit,Rabbit,54,Orange,Small,20.25695796022821,1,0,86,62,0,0 +628,Cat,Siamese,125,White,Medium,7.889240422465326,1,0,39,296,0,1 +629,Bird,Parakeet,131,Brown,Large,15.18909857049649,1,0,51,244,1,0 +630,Bird,Parakeet,169,White,Medium,9.296607738906557,0,0,9,6,0,0 +631,Dog,Golden Retriever,134,Black,Small,21.960291604313074,0,0,23,392,1,0 +632,Bird,Parakeet,25,White,Medium,28.413904167297463,1,0,12,259,0,1 +633,Dog,Labrador,141,Gray,Large,27.595226604624585,1,0,88,88,0,1 +634,Rabbit,Rabbit,117,Brown,Small,17.91936023917864,1,0,62,176,0,0 +635,Bird,Parakeet,136,White,Large,14.415698794452373,1,0,73,203,1,0 +636,Bird,Parakeet,149,Brown,Small,24.730714773742072,1,0,77,133,1,0 +637,Bird,Parakeet,25,Brown,Small,20.816464516204167,1,0,40,81,1,0 +638,Cat,Persian,79,Black,Small,6.323359312291053,1,0,54,487,0,0 +639,Cat,Persian,68,Black,Medium,24.806657194777006,0,0,81,20,0,0 +640,Dog,Poodle,173,Gray,Large,13.437647292446595,1,0,7,398,1,0 +641,Dog,Labrador,12,Orange,Large,6.480584139164078,1,0,17,127,1,1 +642,Cat,Persian,32,Brown,Large,9.17212316149828,0,1,50,194,1,0 +643,Rabbit,Rabbit,128,Orange,Small,8.758283877201928,1,1,24,193,0,0 +644,Rabbit,Rabbit,96,Gray,Medium,16.057002263500344,0,0,2,183,1,0 +645,Rabbit,Rabbit,92,Brown,Small,7.79673262965839,1,1,83,246,0,0 +646,Rabbit,Rabbit,141,Brown,Large,4.583428134645709,1,0,33,360,0,0 +647,Cat,Persian,31,Orange,Medium,4.587034667799061,1,0,21,359,0,1 +648,Bird,Parakeet,78,White,Medium,17.9150097170338,1,0,74,323,0,1 +649,Rabbit,Rabbit,20,Black,Medium,22.30801937663198,1,1,53,159,1,1 +650,Cat,Siamese,146,White,Large,10.324457256311883,1,0,46,193,1,0 +651,Rabbit,Rabbit,60,Orange,Medium,2.004870584606495,1,1,45,109,1,0 +652,Dog,Golden Retriever,134,Black,Medium,4.689521721173311,1,0,9,412,0,1 +653,Cat,Siamese,160,White,Small,27.526142249774654,1,0,6,65,0,0 +654,Dog,Labrador,93,Black,Small,15.312766128094138,1,0,51,417,1,1 +655,Bird,Parakeet,43,Brown,Medium,14.153611478840068,0,0,35,253,0,0 +656,Cat,Siamese,130,White,Medium,2.1034261709926367,1,0,82,282,1,1 +657,Rabbit,Rabbit,60,Black,Small,22.130473647190072,0,0,53,59,0,0 +658,Dog,Labrador,151,Black,Small,16.31397434703029,0,0,19,117,0,0 +659,Bird,Parakeet,31,Black,Small,6.860347146509103,1,0,69,422,0,0 +660,Bird,Parakeet,5,White,Large,4.192139045250658,0,0,25,43,1,0 +661,Cat,Persian,162,Gray,Large,12.118248045327956,1,1,11,485,0,0 +662,Bird,Parakeet,10,White,Small,25.277696961655742,1,0,74,209,0,1 +663,Dog,Poodle,89,Orange,Medium,26.09354221682971,1,0,17,35,1,1 +664,Dog,Labrador,130,White,Small,18.482545031473457,1,0,36,18,0,1 +665,Bird,Parakeet,126,Black,Large,22.098556389674137,1,0,21,90,1,0 +666,Cat,Persian,11,Brown,Medium,4.339517834683241,0,1,18,300,0,0 +667,Rabbit,Rabbit,178,White,Large,18.05565589856779,1,1,60,211,0,0 +668,Bird,Parakeet,159,White,Medium,20.342005401128272,1,0,18,404,1,1 +669,Bird,Parakeet,56,Black,Large,6.820672444499974,1,0,1,406,0,0 +670,Cat,Persian,83,Brown,Medium,26.52448725439751,1,1,14,221,0,0 +671,Rabbit,Rabbit,137,Brown,Medium,6.654905388109573,1,1,8,216,0,0 +672,Rabbit,Rabbit,99,Orange,Medium,10.090843435547063,1,1,66,222,0,0 +673,Dog,Labrador,149,White,Medium,18.9961485636803,1,1,51,410,0,1 +674,Cat,Siamese,73,Brown,Large,19.51215188879119,0,0,79,97,0,0 +675,Cat,Persian,156,Brown,Large,21.28215748863365,1,0,60,455,1,0 +676,Rabbit,Rabbit,115,Gray,Medium,4.248339678977965,1,0,34,18,1,1 +677,Cat,Siamese,7,Gray,Medium,25.634468467928105,0,0,36,296,0,1 +678,Cat,Siamese,80,Brown,Large,16.452566836214537,1,0,62,25,0,0 +679,Dog,Labrador,144,Orange,Medium,14.668332029046983,1,0,1,27,0,1 +680,Dog,Labrador,97,Brown,Medium,29.451831110891778,1,0,85,63,0,1 +681,Bird,Parakeet,4,Black,Medium,22.470970611289573,0,0,42,323,0,1 +682,Dog,Poodle,4,White,Small,4.422174771961871,1,0,1,222,0,1 +683,Cat,Persian,103,Orange,Medium,22.93963584956098,0,0,84,79,0,0 +684,Cat,Siamese,40,Black,Small,3.762219085213215,1,0,54,50,0,0 +685,Dog,Labrador,17,Orange,Medium,1.7966463128765606,0,1,47,457,0,1 +686,Dog,Golden Retriever,101,Black,Large,12.705150962827016,1,1,39,101,1,0 +687,Cat,Persian,103,White,Small,29.813767011757342,1,0,30,175,1,0 +688,Dog,Poodle,124,White,Small,6.071634149651016,1,0,7,9,0,0 +689,Rabbit,Rabbit,52,Gray,Medium,20.50668596227691,0,0,22,236,1,0 +690,Cat,Persian,52,Black,Medium,6.028485919977461,1,0,50,456,0,1 +691,Bird,Parakeet,164,Gray,Small,22.018822273793194,1,0,45,422,1,0 +692,Rabbit,Rabbit,124,Black,Large,15.655256378896958,1,0,74,99,0,0 +693,Bird,Parakeet,166,Gray,Large,20.719179592591914,1,1,72,498,1,0 +694,Bird,Parakeet,178,White,Medium,6.883322101512803,1,0,10,377,0,1 +695,Rabbit,Rabbit,63,Orange,Large,22.623221643094556,1,0,14,227,0,0 +696,Bird,Parakeet,116,Gray,Large,29.872952729058493,0,0,46,402,0,0 +697,Dog,Golden Retriever,62,Black,Medium,20.30386451815283,0,0,75,63,0,0 +698,Cat,Persian,7,Black,Large,15.310919944113452,1,0,6,379,0,1 +699,Dog,Poodle,155,White,Medium,27.77204566995868,0,1,21,18,0,0 +700,Cat,Persian,92,Black,Small,1.4597034247244127,1,0,62,331,1,0 +701,Bird,Parakeet,14,Brown,Medium,22.16778205928881,1,0,19,317,0,1 +702,Bird,Parakeet,86,Orange,Small,26.773648987600573,1,0,88,140,0,0 +703,Rabbit,Rabbit,133,Gray,Small,2.788562925007766,0,0,82,24,0,0 +704,Rabbit,Rabbit,130,Orange,Large,14.051860942056987,0,0,62,227,1,0 +705,Dog,Labrador,62,White,Large,6.936864164549968,1,0,83,88,1,1 +706,Rabbit,Rabbit,149,Gray,Large,17.429026802047073,1,0,53,91,1,0 +707,Bird,Parakeet,79,Gray,Medium,19.836745799506154,1,0,24,41,0,1 +708,Cat,Persian,149,Brown,Large,9.776072304933896,0,1,56,372,0,0 +709,Dog,Golden Retriever,138,White,Small,14.339468420664923,0,0,62,294,0,0 +710,Rabbit,Rabbit,58,Black,Large,9.100845073020226,0,0,34,413,0,0 +711,Rabbit,Rabbit,120,Brown,Large,11.712343769198299,1,0,22,388,0,0 +712,Bird,Parakeet,167,Brown,Medium,5.626658743964284,0,0,68,80,0,0 +713,Rabbit,Rabbit,133,Orange,Medium,26.681223863983313,0,0,57,401,0,0 +714,Bird,Parakeet,65,Orange,Small,1.2903019062149563,0,0,10,106,0,0 +715,Cat,Siamese,13,Orange,Medium,26.589866406869866,1,0,66,110,0,1 +716,Bird,Parakeet,82,Brown,Large,17.578687472142416,1,0,8,276,1,0 +717,Bird,Parakeet,173,Black,Medium,10.618077481133508,1,0,38,213,0,1 +718,Rabbit,Rabbit,160,Gray,Small,21.96865663834956,0,0,62,230,1,0 +719,Rabbit,Rabbit,164,Orange,Large,15.724812404017266,1,0,28,305,0,0 +720,Bird,Parakeet,139,Black,Small,15.474151085120589,1,0,10,120,0,0 +721,Rabbit,Rabbit,149,White,Medium,4.1941262046500265,0,1,45,133,0,0 +722,Dog,Labrador,52,Orange,Medium,12.295964752416852,0,0,65,305,1,1 +723,Rabbit,Rabbit,117,Gray,Small,7.990866029845362,1,0,67,207,0,0 +724,Rabbit,Rabbit,26,White,Large,1.4551812622921099,0,0,9,449,1,0 +725,Bird,Parakeet,34,White,Medium,11.442889292681285,0,0,5,370,0,0 +726,Cat,Persian,52,Gray,Medium,3.6048417533425376,1,0,10,256,0,1 +727,Dog,Golden Retriever,112,Orange,Large,8.448071094030372,1,1,12,184,1,0 +728,Rabbit,Rabbit,82,Brown,Medium,3.682982014309739,1,0,82,285,0,1 +729,Dog,Labrador,157,Gray,Small,3.9273071411355667,1,1,58,441,1,0 +730,Cat,Siamese,166,Gray,Medium,6.77132753804046,1,0,21,362,0,1 +731,Bird,Parakeet,42,Gray,Medium,29.995628090105605,1,0,21,179,0,1 +732,Cat,Persian,21,White,Large,1.0633401428602145,0,0,65,420,1,0 +733,Bird,Parakeet,42,Black,Small,19.521506447491753,1,0,3,47,1,0 +734,Bird,Parakeet,141,Brown,Large,5.096067242770163,0,0,69,86,0,0 +735,Rabbit,Rabbit,113,Orange,Medium,27.698215964612498,1,1,89,384,1,0 +736,Rabbit,Rabbit,105,Orange,Medium,3.7153539066752477,1,0,64,18,0,1 +737,Cat,Persian,17,Black,Large,28.69683242666086,1,1,49,45,1,0 +738,Dog,Labrador,120,Gray,Large,18.716865237020862,1,1,72,450,0,0 +739,Cat,Siamese,137,Brown,Large,1.0367742305492171,1,1,17,385,0,0 +740,Rabbit,Rabbit,169,Orange,Small,26.926208739587377,1,0,73,55,0,0 +741,Cat,Persian,129,Orange,Medium,16.12964223964063,1,0,32,228,0,1 +742,Dog,Labrador,130,Gray,Large,8.617422324584052,1,0,70,144,0,1 +743,Rabbit,Rabbit,60,Black,Medium,23.251096147953273,1,0,21,91,0,1 +744,Bird,Parakeet,48,Brown,Small,27.87330106271923,1,0,7,81,0,0 +745,Bird,Parakeet,29,Black,Small,15.027033871257023,1,0,7,224,1,0 +746,Dog,Labrador,97,Black,Small,10.151050758850216,1,0,27,78,0,1 +747,Bird,Parakeet,10,Brown,Small,23.879408594112878,0,1,8,149,0,0 +748,Bird,Parakeet,58,Brown,Medium,20.771779435865184,1,1,73,360,0,0 +749,Rabbit,Rabbit,21,Orange,Small,26.7738220287579,1,0,28,282,1,1 +750,Rabbit,Rabbit,28,Orange,Small,21.082906370074596,1,0,79,310,0,0 +751,Bird,Parakeet,34,Brown,Large,8.062321828086553,1,0,66,484,0,0 +752,Rabbit,Rabbit,8,White,Small,28.018118243011553,1,0,71,159,0,1 +753,Rabbit,Rabbit,177,Orange,Large,4.040236668973748,0,0,47,315,0,0 +754,Cat,Persian,79,White,Small,8.770339753098588,0,0,82,364,0,0 +755,Dog,Golden Retriever,145,Brown,Small,16.08564362399268,1,0,86,466,0,0 +756,Dog,Poodle,87,Orange,Large,17.40651875605614,0,0,10,155,0,0 +757,Rabbit,Rabbit,88,Black,Small,1.4440070121802575,1,0,33,155,0,0 +758,Rabbit,Rabbit,173,Brown,Medium,23.63660904880726,1,0,7,375,0,1 +759,Cat,Siamese,69,Gray,Medium,11.486612234336295,1,1,3,201,0,0 +760,Rabbit,Rabbit,160,White,Small,12.786137689061997,1,0,14,467,0,0 +761,Cat,Siamese,172,Brown,Medium,24.471716788671426,0,0,44,408,1,0 +762,Bird,Parakeet,87,Brown,Large,11.999218360098793,1,0,46,340,1,0 +763,Dog,Labrador,101,Orange,Small,2.36952141834789,1,0,61,461,0,1 +764,Cat,Persian,8,Orange,Small,6.116753343640159,1,0,36,341,0,1 +765,Cat,Persian,77,Orange,Large,28.72119199925729,0,0,13,116,0,0 +766,Cat,Persian,87,Gray,Large,3.0417555096729108,1,0,16,153,0,0 +767,Bird,Parakeet,43,Orange,Large,11.29300482807247,0,0,80,363,0,0 +768,Rabbit,Rabbit,129,Brown,Small,15.592195844488993,1,0,5,330,0,0 +769,Dog,Labrador,109,Orange,Large,15.305143992818241,0,0,89,118,1,0 +770,Rabbit,Rabbit,179,Black,Large,1.1148383473521397,0,0,38,198,1,0 +771,Dog,Labrador,152,Orange,Medium,15.53371921226722,0,0,50,400,0,1 +772,Cat,Siamese,152,Gray,Medium,12.129401281344867,1,0,38,465,0,1 +773,Rabbit,Rabbit,17,Black,Small,29.190300728762157,1,1,56,262,1,0 +774,Rabbit,Rabbit,133,Brown,Medium,5.615613435821065,1,1,63,183,1,0 +775,Rabbit,Rabbit,148,Brown,Medium,11.270471706025354,1,0,48,447,1,1 +776,Rabbit,Rabbit,21,White,Large,13.164963067604774,1,1,59,147,0,0 +777,Rabbit,Rabbit,58,Black,Large,12.330112575927515,1,0,77,165,0,0 +778,Bird,Parakeet,159,White,Medium,20.309094001904015,1,1,44,74,0,0 +779,Bird,Parakeet,110,Black,Medium,7.249872868885296,0,0,55,235,0,0 +780,Dog,Labrador,139,Black,Medium,7.411075031605763,1,0,55,313,0,1 +781,Rabbit,Rabbit,143,Gray,Small,22.14711750652474,1,1,1,271,0,0 +782,Rabbit,Rabbit,135,White,Small,15.50842504166721,1,0,8,178,1,0 +783,Bird,Parakeet,28,Gray,Small,13.039514605662001,1,0,4,282,1,0 +784,Bird,Parakeet,51,Black,Medium,5.906888498640303,0,0,85,50,0,0 +785,Cat,Siamese,5,Brown,Medium,6.438289464452832,0,0,86,74,1,1 +786,Cat,Persian,5,Black,Small,1.3059974676611157,1,0,53,143,0,1 +787,Cat,Persian,94,Gray,Small,3.6513617969895678,0,0,58,342,1,0 +788,Cat,Siamese,96,White,Medium,17.14832214601699,0,1,37,187,0,0 +789,Bird,Parakeet,8,White,Large,26.864320770243886,0,0,49,122,0,0 +790,Cat,Persian,64,Gray,Small,9.327778188786166,1,1,67,331,0,0 +791,Bird,Parakeet,138,White,Medium,10.504669550987716,0,1,19,250,0,0 +792,Bird,Parakeet,38,Orange,Medium,4.352562707733854,1,0,1,295,0,1 +793,Cat,Siamese,111,Brown,Large,12.27029742260598,1,0,29,322,0,0 +794,Cat,Siamese,112,Brown,Medium,2.079429432510546,1,1,87,129,1,0 +795,Rabbit,Rabbit,81,Brown,Medium,27.610420191595107,0,0,33,251,0,0 +796,Bird,Parakeet,136,White,Small,20.52022909561133,1,0,52,357,0,0 +797,Rabbit,Rabbit,104,White,Small,29.731715472403348,1,0,41,431,0,0 +798,Dog,Labrador,153,Black,Large,26.601871717676318,1,0,23,179,0,1 +799,Rabbit,Rabbit,73,Black,Large,14.716727217246428,1,0,5,407,0,0 +800,Dog,Golden Retriever,178,Brown,Medium,16.070865903360932,1,0,71,348,1,1 +801,Rabbit,Rabbit,49,Orange,Small,6.526705348998622,0,0,57,494,0,0 +802,Bird,Parakeet,36,Black,Large,14.399140942347113,1,0,6,109,0,0 +803,Rabbit,Rabbit,126,Black,Large,27.18780833129154,1,1,49,481,1,0 +804,Dog,Labrador,68,White,Large,2.4747492854388127,0,0,22,467,0,0 +805,Rabbit,Rabbit,78,Orange,Medium,26.043147286950983,1,1,77,337,0,0 +806,Rabbit,Rabbit,105,Brown,Small,10.887486247635682,1,0,76,77,1,0 +807,Cat,Siamese,169,Black,Large,27.085870187792985,0,0,4,465,0,0 +808,Cat,Siamese,122,Orange,Small,28.285517283903054,0,0,72,1,0,0 +809,Cat,Siamese,119,Orange,Large,20.455081723055272,0,1,64,320,1,0 +810,Rabbit,Rabbit,114,Brown,Large,20.268289811187657,1,0,37,275,0,0 +811,Dog,Labrador,10,Orange,Small,14.129875793553461,0,0,31,289,0,1 +812,Rabbit,Rabbit,149,Orange,Small,25.230666511767517,0,1,37,296,1,0 +813,Dog,Labrador,164,Orange,Small,28.886769994565213,0,0,45,168,0,0 +814,Cat,Siamese,140,Black,Medium,23.477372209834456,1,0,8,239,0,1 +815,Bird,Parakeet,91,Brown,Large,19.036165278944566,1,0,40,458,1,0 +816,Bird,Parakeet,2,Orange,Medium,18.596993978818766,1,1,5,474,1,1 +817,Rabbit,Rabbit,118,Orange,Small,25.634627145926206,0,0,28,22,0,0 +818,Dog,Golden Retriever,13,Black,Large,16.69490920736765,1,0,81,293,0,1 +819,Dog,Labrador,160,Brown,Medium,24.311936701215856,1,1,87,171,1,1 +820,Rabbit,Rabbit,95,White,Large,15.757672400053128,1,0,9,322,1,0 +821,Bird,Parakeet,144,Black,Medium,2.8108839742438,1,0,2,317,0,1 +822,Dog,Golden Retriever,32,Brown,Small,12.134463258220501,1,0,63,395,0,0 +823,Rabbit,Rabbit,32,Orange,Medium,13.888787772473464,1,0,51,285,0,1 +824,Cat,Siamese,124,Brown,Large,5.106900104611899,0,0,31,478,1,0 +825,Cat,Siamese,11,Brown,Large,3.9723935464871714,1,1,7,342,1,0 +826,Cat,Persian,10,Brown,Large,16.52848354005167,1,0,30,192,1,1 +827,Bird,Parakeet,15,White,Large,3.469165460126382,1,0,58,168,1,1 +828,Rabbit,Rabbit,17,Brown,Small,9.785409868812435,1,0,3,403,0,1 +829,Cat,Persian,168,White,Medium,22.593312302929203,1,0,51,117,0,1 +830,Cat,Persian,146,Orange,Small,28.96853320747144,0,0,56,5,1,0 +831,Bird,Parakeet,153,White,Small,21.818246836005926,0,0,73,330,1,0 +832,Cat,Siamese,59,Orange,Small,29.786140905133237,1,0,10,482,0,0 +833,Cat,Siamese,78,Orange,Large,21.251801167102677,1,0,61,131,0,0 +834,Bird,Parakeet,124,White,Large,9.520039648975498,0,0,50,92,1,0 +835,Cat,Persian,114,Black,Large,20.489523674655494,0,0,47,250,0,0 +836,Cat,Siamese,22,Gray,Medium,21.362546190710212,1,1,1,9,0,1 +837,Rabbit,Rabbit,158,Brown,Large,11.780082553877357,1,0,57,50,0,0 +838,Dog,Labrador,172,Gray,Medium,1.5137016676722814,1,0,38,348,0,1 +839,Bird,Parakeet,32,Brown,Large,21.39279375848912,1,1,62,293,0,0 +840,Cat,Persian,179,Brown,Large,1.629302762990707,1,0,79,128,1,0 +841,Bird,Parakeet,174,Orange,Small,19.304454263742063,1,0,89,487,0,0 +842,Dog,Poodle,113,Brown,Small,5.744878173534097,1,0,70,391,0,0 +843,Bird,Parakeet,120,Brown,Small,17.402502304693257,1,0,82,248,0,0 +844,Cat,Siamese,149,White,Medium,15.6134777360374,1,0,65,101,1,1 +845,Cat,Siamese,122,Gray,Medium,1.3918956268792946,1,1,62,336,1,0 +846,Rabbit,Rabbit,32,White,Large,11.523108692858,1,0,56,82,1,0 +847,Dog,Poodle,119,Brown,Medium,18.43164385449278,1,0,67,190,1,1 +848,Bird,Parakeet,39,Orange,Large,25.609590005064614,0,0,17,416,0,0 +849,Dog,Labrador,12,White,Medium,26.770621301259002,0,1,8,142,0,1 +850,Rabbit,Rabbit,175,White,Medium,15.031649130963475,0,0,60,236,0,0 +851,Dog,Poodle,18,Black,Medium,22.124252131683708,0,1,40,286,0,0 +852,Rabbit,Rabbit,149,Brown,Small,10.177182719470272,0,0,81,276,1,0 +853,Cat,Persian,141,Orange,Medium,10.923405767001066,1,0,89,386,0,1 +854,Bird,Parakeet,149,White,Small,6.639442056287098,1,0,62,381,1,0 +855,Bird,Parakeet,126,White,Small,17.296495189803938,1,0,41,449,0,0 +856,Bird,Parakeet,136,Orange,Medium,10.122360376893592,0,0,12,290,0,0 +857,Rabbit,Rabbit,46,Brown,Large,3.446755996716646,1,0,89,168,0,0 +858,Cat,Persian,104,Black,Large,20.865176513232182,1,0,54,442,0,0 +859,Rabbit,Rabbit,135,White,Large,21.524843020556546,1,0,89,143,0,0 +860,Bird,Parakeet,151,Black,Medium,11.439780144592678,1,1,11,183,0,0 +861,Bird,Parakeet,86,Gray,Large,3.224978584224661,0,1,60,44,0,0 +862,Cat,Siamese,150,Brown,Small,13.362633804699351,0,0,68,242,0,0 +863,Dog,Poodle,89,Gray,Large,13.82772828345625,1,0,78,314,0,0 +864,Bird,Parakeet,22,Gray,Medium,20.391455662038897,0,0,46,433,0,1 +865,Cat,Siamese,92,Gray,Large,10.14707534650182,1,0,38,468,0,0 +866,Cat,Persian,29,Brown,Small,20.79537911365786,1,1,61,267,0,0 +867,Cat,Persian,13,Brown,Medium,26.273167710964263,0,0,17,475,0,1 +868,Dog,Poodle,2,Orange,Large,7.887095228503795,0,1,66,252,0,0 +869,Cat,Siamese,29,Black,Medium,29.388667127477092,1,1,72,395,0,0 +870,Cat,Siamese,27,Black,Medium,12.204675976967401,1,1,36,288,0,0 +871,Rabbit,Rabbit,50,Orange,Medium,8.750501869264792,1,0,74,211,0,1 +872,Dog,Labrador,95,Orange,Large,13.223824292798424,0,0,7,487,0,0 +873,Rabbit,Rabbit,151,Gray,Medium,3.737526801211916,0,0,16,176,0,0 +874,Dog,Labrador,88,Orange,Large,4.645471288851354,0,0,14,20,1,0 +875,Bird,Parakeet,44,White,Small,13.018587362081611,0,0,80,315,0,0 +876,Rabbit,Rabbit,22,Black,Large,23.110891136233903,1,0,25,51,0,1 +877,Cat,Siamese,171,Orange,Small,27.109054947660237,0,0,4,437,1,0 +878,Rabbit,Rabbit,165,White,Large,20.279721730771858,1,0,29,422,0,0 +879,Rabbit,Rabbit,97,White,Medium,4.051367101906922,1,0,56,232,1,1 +880,Cat,Siamese,3,White,Large,20.488573103235517,0,0,6,432,0,0 +881,Bird,Parakeet,118,White,Medium,19.427409700386185,1,0,1,122,0,1 +882,Bird,Parakeet,19,Gray,Small,24.367153630506607,1,0,39,361,1,1 +883,Rabbit,Rabbit,23,White,Large,24.932211332913244,1,0,1,447,0,1 +884,Rabbit,Rabbit,157,Gray,Medium,26.17788919759039,1,0,63,99,0,1 +885,Rabbit,Rabbit,30,Gray,Small,13.009813922793853,1,0,57,213,0,0 +886,Dog,Poodle,39,White,Small,21.238067465212183,1,0,67,409,0,0 +887,Rabbit,Rabbit,108,Black,Large,8.779260408747012,1,0,65,326,0,0 +888,Cat,Persian,111,White,Small,29.980752119521625,1,0,65,134,0,0 +889,Bird,Parakeet,25,White,Medium,6.084692999928938,1,0,21,455,1,1 +890,Dog,Golden Retriever,128,White,Small,1.2933185181642384,1,0,30,317,1,0 +891,Bird,Parakeet,111,Black,Small,5.315452721027104,1,0,33,217,0,0 +892,Rabbit,Rabbit,136,Brown,Medium,23.500296396779675,1,0,48,87,1,1 +893,Cat,Siamese,106,Brown,Small,13.956726618738132,1,1,22,279,0,0 +894,Cat,Siamese,144,Gray,Small,13.97750718650242,0,0,54,39,0,0 +895,Cat,Siamese,72,Gray,Small,28.695664668738388,1,1,4,239,1,0 +896,Cat,Siamese,129,Black,Small,27.669412332814154,1,0,87,331,1,0 +897,Bird,Parakeet,108,Orange,Medium,10.024647396316267,1,0,55,430,1,1 +898,Dog,Labrador,5,Brown,Small,7.062685734645118,1,0,44,135,0,1 +899,Dog,Poodle,3,Gray,Medium,25.60598408807845,1,0,89,181,0,1 +900,Cat,Siamese,132,Brown,Small,27.274553060286532,1,0,12,264,1,0 +901,Bird,Parakeet,104,Black,Small,16.70631076115612,0,0,81,258,0,0 +902,Cat,Persian,64,Black,Small,19.253287622395913,1,0,79,181,0,0 +903,Cat,Persian,106,Orange,Small,8.450514256076508,1,0,74,20,0,0 +904,Rabbit,Rabbit,98,Orange,Medium,24.223277951377213,1,0,52,350,1,1 +905,Dog,Labrador,10,Black,Medium,19.33155042502167,1,1,76,431,0,1 +906,Dog,Labrador,149,Gray,Medium,15.601891827641902,1,0,65,430,0,1 +907,Rabbit,Rabbit,128,Brown,Large,2.743148126273479,0,0,65,471,0,0 +908,Dog,Golden Retriever,46,White,Small,27.562113912823456,0,0,10,195,0,0 +909,Rabbit,Rabbit,155,Orange,Small,18.848091191147773,1,0,18,211,1,0 +910,Bird,Parakeet,66,White,Medium,6.588041746096071,0,1,47,446,0,0 +911,Dog,Poodle,169,Gray,Large,18.58519739250853,1,0,66,15,0,0 +912,Dog,Golden Retriever,112,Orange,Small,20.64643727295923,1,0,49,201,0,0 +913,Bird,Parakeet,13,White,Medium,16.24733976381354,1,0,72,362,0,1 +914,Rabbit,Rabbit,137,Gray,Small,21.824950400724653,0,0,22,217,0,0 +915,Cat,Persian,164,Brown,Small,17.8621644899301,1,0,86,92,0,0 +916,Bird,Parakeet,86,Gray,Medium,28.4853178789323,1,1,26,132,0,0 +917,Dog,Poodle,90,Orange,Medium,2.557215279379898,1,0,9,138,0,1 +918,Dog,Labrador,58,Black,Small,4.081125545617255,1,1,21,156,1,0 +919,Cat,Persian,54,Brown,Small,6.036477471675855,0,1,63,492,1,0 +920,Dog,Golden Retriever,12,White,Medium,21.01496104752563,1,0,6,334,0,1 +921,Dog,Poodle,14,Orange,Small,19.722280309744452,1,0,14,69,0,1 +922,Cat,Persian,121,Black,Small,26.869706946081127,1,0,33,303,1,0 +923,Rabbit,Rabbit,162,Gray,Medium,22.25344613363305,1,0,71,14,0,1 +924,Dog,Poodle,121,Black,Small,15.742309139373175,1,0,24,234,0,0 +925,Cat,Persian,20,Orange,Small,29.763121981445277,0,1,73,211,1,0 +926,Dog,Golden Retriever,75,Orange,Large,8.717130728909677,0,0,75,289,0,0 +927,Rabbit,Rabbit,144,Black,Medium,25.162558133778166,1,0,87,59,0,1 +928,Dog,Poodle,70,Gray,Large,13.376920793623661,1,0,51,251,0,0 +929,Cat,Siamese,56,Black,Large,19.813319027396293,0,0,2,445,0,0 +930,Bird,Parakeet,101,Orange,Large,22.090785643120586,1,1,15,1,1,0 +931,Cat,Persian,132,Orange,Small,19.378868528519753,1,0,35,235,0,0 +932,Cat,Siamese,111,White,Small,5.074711790149205,1,0,36,335,0,0 +933,Rabbit,Rabbit,125,White,Large,13.84557931169005,1,0,39,18,1,0 +934,Cat,Persian,132,Brown,Large,17.281712960060606,1,1,4,481,1,0 +935,Bird,Parakeet,20,Orange,Large,25.622269016179306,1,0,64,48,0,1 +936,Cat,Persian,138,White,Small,13.865726715158598,1,1,74,77,0,0 +937,Bird,Parakeet,24,Black,Large,27.643671839323293,1,0,6,471,0,0 +938,Cat,Persian,154,Gray,Large,13.77085246773111,1,0,57,27,1,0 +939,Dog,Labrador,37,Black,Medium,16.914615918627337,0,0,30,47,0,1 +940,Dog,Golden Retriever,54,Gray,Medium,24.09228480534123,0,0,74,95,0,0 +941,Cat,Siamese,103,White,Small,17.908962848242165,1,0,67,164,0,0 +942,Rabbit,Rabbit,149,Brown,Medium,28.846324099101913,1,0,15,457,0,1 +943,Rabbit,Rabbit,38,Black,Medium,20.1497390849097,1,0,17,432,0,1 +944,Rabbit,Rabbit,174,Black,Medium,16.15857416796043,1,0,18,364,0,1 +945,Bird,Parakeet,4,Black,Small,1.68416706564308,1,0,19,175,0,1 +946,Rabbit,Rabbit,57,Gray,Medium,12.998254029829566,1,0,76,72,0,1 +947,Rabbit,Rabbit,173,White,Small,9.678204158544652,1,0,88,471,0,0 +948,Rabbit,Rabbit,20,Black,Large,22.69506436873719,1,0,69,355,0,1 +949,Cat,Siamese,17,Black,Medium,7.040052579452828,0,0,24,28,1,1 +950,Rabbit,Rabbit,119,Brown,Small,6.951382531528083,1,0,48,443,1,0 +951,Dog,Labrador,71,Brown,Medium,6.496052253883927,1,0,30,156,0,1 +952,Dog,Golden Retriever,4,White,Large,4.642852781530841,1,1,15,313,0,0 +953,Bird,Parakeet,14,White,Small,10.019203106455734,1,0,47,303,0,1 +954,Dog,Golden Retriever,3,Orange,Small,26.691741318859325,1,0,44,278,0,1 +955,Dog,Labrador,80,Brown,Small,23.472925696872693,1,1,27,282,0,0 +956,Rabbit,Rabbit,55,Orange,Large,22.32228527289701,1,1,84,478,0,0 +957,Cat,Siamese,132,Gray,Large,24.12276736384892,0,0,64,490,1,0 +958,Rabbit,Rabbit,120,Orange,Large,8.813118790557578,1,1,41,6,0,0 +959,Dog,Labrador,24,Black,Small,26.735809031822146,0,0,68,304,0,0 +960,Rabbit,Rabbit,162,Orange,Large,4.981797879106527,1,0,5,181,0,0 +961,Bird,Parakeet,158,White,Medium,10.754026074751875,0,0,53,108,1,0 +962,Cat,Persian,82,Gray,Large,18.015695450052945,1,0,8,323,0,0 +963,Dog,Golden Retriever,147,Black,Small,3.984247561918018,0,0,22,205,1,0 +964,Cat,Siamese,159,Orange,Medium,22.173775445516323,0,1,7,233,0,0 +965,Bird,Parakeet,60,Gray,Large,12.230135397900774,1,1,22,425,1,0 +966,Dog,Poodle,147,Gray,Medium,23.747764302643603,1,0,46,457,1,1 +967,Rabbit,Rabbit,49,Black,Medium,15.29399228195348,0,0,30,186,1,0 +968,Dog,Poodle,63,Orange,Small,6.245361208334755,1,0,16,427,0,0 +969,Cat,Persian,110,Gray,Medium,22.308092529304986,1,0,79,481,0,1 +970,Cat,Persian,150,Gray,Medium,8.312174719329363,1,0,5,409,0,1 +971,Bird,Parakeet,152,Brown,Small,15.233498736773242,0,0,67,406,1,0 +972,Dog,Golden Retriever,109,Gray,Small,3.1904648055503206,1,1,23,328,0,0 +973,Rabbit,Rabbit,76,Brown,Small,18.918132977949064,0,0,61,356,0,0 +974,Cat,Siamese,30,Orange,Medium,20.30987522027204,1,0,74,7,0,1 +975,Cat,Siamese,58,Orange,Medium,11.981552089210723,0,0,5,52,0,0 +976,Bird,Parakeet,117,Orange,Medium,28.678260629311144,1,0,23,397,0,1 +977,Dog,Labrador,159,White,Small,26.350898722979984,0,1,79,198,0,0 +978,Dog,Labrador,100,Orange,Large,21.53028655645985,1,0,82,390,0,1 +979,Dog,Poodle,19,Gray,Medium,21.695910160318537,1,0,25,136,0,1 +980,Dog,Labrador,176,Black,Large,8.319649733322386,0,0,8,258,1,0 +981,Rabbit,Rabbit,173,Brown,Small,20.04764170156502,0,0,21,333,1,0 +982,Rabbit,Rabbit,94,White,Medium,6.74798169183007,1,0,2,400,1,1 +983,Bird,Parakeet,113,Orange,Large,5.176093493674841,1,0,22,497,0,0 +984,Dog,Golden Retriever,146,Orange,Medium,29.984568931874826,1,0,72,171,1,1 +985,Rabbit,Rabbit,63,Black,Medium,19.04890201272205,1,0,87,85,0,1 +986,Rabbit,Rabbit,116,Black,Medium,13.498648307058525,1,0,72,45,0,1 +987,Cat,Siamese,113,White,Large,9.356771163634589,1,0,68,345,0,0 +988,Bird,Parakeet,47,Brown,Small,18.513692866204593,0,0,64,185,0,0 +989,Rabbit,Rabbit,57,Orange,Medium,2.4382117383824977,1,0,19,155,0,1 +990,Rabbit,Rabbit,118,White,Small,3.7457487246390566,0,1,87,492,1,0 +991,Bird,Parakeet,45,Gray,Small,1.952844234471494,1,0,82,397,0,0 +992,Cat,Persian,161,Gray,Large,15.475925164617191,1,0,53,8,0,0 +993,Dog,Labrador,145,White,Large,18.417592246428296,1,0,87,114,0,1 +994,Bird,Parakeet,4,Orange,Small,2.811913079376805,1,0,52,153,0,1 +995,Dog,Golden Retriever,148,White,Medium,21.91550955746858,1,0,47,129,0,1 +996,Dog,Golden Retriever,47,Orange,Large,7.681457073922368,1,1,82,330,0,0 +997,Dog,Poodle,143,Black,Medium,20.870842159326116,0,0,54,80,1,0 +998,Dog,Labrador,47,White,Medium,29.475017061022122,1,0,44,383,0,1 +999,Cat,Persian,78,Orange,Medium,21.042070231561684,1,0,7,289,0,1 +1000,Rabbit,Rabbit,124,White,Small,26.612598534784645,1,1,1,146,1,0 +1001,Dog,Poodle,75,White,Medium,28.497756695548954,0,0,63,294,1,0 +1002,Dog,Golden Retriever,103,Orange,Small,15.115780402217151,0,0,39,465,1,0 +1003,Bird,Parakeet,137,Orange,Small,21.367424403171498,0,1,19,268,0,0 +1004,Dog,Labrador,90,Brown,Medium,25.70710835311941,0,0,31,132,0,1 +1005,Dog,Golden Retriever,144,White,Medium,11.341658473357198,1,0,77,49,0,1 +1006,Rabbit,Rabbit,69,Gray,Medium,8.580522180251451,1,0,24,22,1,1 +1007,Dog,Golden Retriever,61,White,Medium,28.451358610571823,1,0,69,377,1,1 +1008,Rabbit,Rabbit,138,White,Small,22.06280151069572,1,0,87,375,0,0 +1009,Cat,Siamese,50,Orange,Medium,7.370781867898963,1,0,83,87,0,1 +1010,Rabbit,Rabbit,3,White,Medium,14.02341838760729,0,0,65,412,1,1 +1011,Rabbit,Rabbit,107,White,Small,24.097101316596472,1,1,33,111,0,0 +1012,Bird,Parakeet,113,Gray,Large,16.86482673663482,1,1,59,110,0,0 +1013,Cat,Siamese,94,Orange,Large,19.848610846810782,0,0,33,71,0,0 +1014,Rabbit,Rabbit,118,White,Small,22.85927785930652,1,0,32,153,0,0 +1015,Dog,Labrador,81,Black,Large,19.038266475460436,1,0,59,462,1,1 +1016,Bird,Parakeet,109,Brown,Small,16.662300335743677,0,0,41,56,0,0 +1017,Rabbit,Rabbit,51,Gray,Medium,22.73881424724543,0,0,12,256,0,0 +1018,Dog,Golden Retriever,147,White,Medium,22.09932802753382,1,0,73,155,0,1 +1019,Dog,Labrador,170,Black,Large,19.266299605312188,0,0,11,112,0,0 +1020,Bird,Parakeet,35,White,Medium,11.433892988075959,1,0,19,317,0,1 +1021,Rabbit,Rabbit,46,White,Large,12.768550673807876,0,0,54,46,0,0 +1022,Dog,Golden Retriever,154,Orange,Large,29.142717917638826,1,0,57,237,0,0 +1023,Dog,Labrador,69,Orange,Medium,4.259192036454249,1,0,42,333,0,1 +1024,Cat,Persian,93,Orange,Medium,8.786642600332225,1,0,71,105,1,1 +1025,Bird,Parakeet,64,Black,Large,15.279651016925735,1,0,31,481,0,0 +1026,Cat,Siamese,165,White,Medium,25.26516089816979,0,0,52,186,0,0 +1027,Cat,Siamese,49,Brown,Small,5.746594248162108,1,0,2,158,1,0 +1028,Bird,Parakeet,149,Gray,Medium,9.031683925613686,1,0,83,197,0,1 +1029,Bird,Parakeet,74,Orange,Medium,2.90814831606193,0,0,38,381,1,0 +1030,Dog,Golden Retriever,5,Orange,Small,21.473572715473736,0,0,32,176,0,0 +1031,Bird,Parakeet,100,White,Medium,7.9080736287928675,1,1,10,260,1,0 +1032,Cat,Siamese,70,White,Medium,12.271567769457564,1,0,8,286,1,1 +1033,Rabbit,Rabbit,90,Black,Large,13.867341963860367,1,0,75,292,0,0 +1034,Dog,Poodle,4,Black,Medium,26.848104132587213,1,0,20,334,1,1 +1035,Cat,Persian,126,Orange,Medium,12.928578419605866,1,1,20,142,1,0 +1036,Dog,Labrador,39,White,Small,5.211525577542223,1,0,9,307,0,1 +1037,Cat,Siamese,163,Brown,Large,16.205133211863508,1,0,72,490,0,0 +1038,Cat,Siamese,129,Gray,Large,28.92578184481026,0,0,15,196,0,0 +1039,Rabbit,Rabbit,24,White,Large,18.62288359047352,1,0,88,475,1,0 +1040,Cat,Siamese,57,White,Medium,29.518229393356606,1,0,63,224,0,1 +1041,Cat,Siamese,100,Black,Large,25.392111959227233,1,0,21,394,0,0 +1042,Cat,Persian,70,Gray,Small,2.0430069301889713,1,1,5,42,0,0 +1043,Dog,Labrador,41,Orange,Large,20.093588265934212,1,0,35,453,0,1 +1044,Dog,Poodle,174,White,Small,9.316239449178626,1,1,85,150,0,0 +1045,Bird,Parakeet,60,Orange,Large,17.22888881369699,0,0,68,295,0,0 +1046,Rabbit,Rabbit,108,White,Large,9.101010537005571,0,1,48,62,1,0 +1047,Rabbit,Rabbit,57,Orange,Small,11.117242081553421,1,0,80,129,0,0 +1048,Dog,Labrador,31,Gray,Large,27.7718926408712,1,0,62,254,0,1 +1049,Cat,Persian,84,Black,Large,6.992652361859454,0,1,35,462,0,0 +1050,Dog,Golden Retriever,152,Gray,Large,26.349164422749084,1,1,70,342,0,0 +1051,Bird,Parakeet,36,Brown,Medium,1.8832764340444075,0,0,21,213,0,0 +1052,Dog,Labrador,158,Black,Small,3.6354196127198026,1,0,80,72,0,1 +1053,Rabbit,Rabbit,152,Gray,Large,25.78557620014364,1,1,39,430,0,0 +1054,Rabbit,Rabbit,108,Brown,Small,19.286124896366665,1,0,34,103,0,0 +1055,Cat,Persian,106,White,Small,22.669848003146956,0,0,15,157,0,0 +1056,Bird,Parakeet,158,White,Small,7.073112453833773,1,1,6,271,0,0 +1057,Bird,Parakeet,86,Orange,Large,16.575420212113002,1,0,66,314,1,0 +1058,Dog,Poodle,135,Black,Large,22.379357483567837,1,0,88,237,0,0 +1059,Bird,Parakeet,163,White,Small,19.19210997329761,1,0,56,302,1,0 +1060,Bird,Parakeet,14,Black,Medium,14.679264239845075,1,0,49,371,1,1 +1061,Cat,Persian,120,White,Small,28.83372980669373,1,0,1,157,0,0 +1062,Bird,Parakeet,138,Orange,Medium,20.608562670149915,0,1,65,108,1,0 +1063,Bird,Parakeet,80,Gray,Small,27.3247732069959,1,0,28,8,0,0 +1064,Cat,Siamese,144,Orange,Small,22.163344835636757,1,1,20,320,0,0 +1065,Dog,Labrador,106,Brown,Small,22.328442329332812,1,1,48,58,0,0 +1066,Cat,Persian,70,White,Large,3.3205298229727007,0,0,72,357,1,0 +1067,Cat,Persian,44,Brown,Medium,11.424474033764364,0,0,65,191,0,0 +1068,Rabbit,Rabbit,73,Brown,Medium,25.892712794870096,0,0,51,135,1,0 +1069,Rabbit,Rabbit,45,Black,Small,7.745512002313922,1,0,43,250,0,0 +1070,Rabbit,Rabbit,107,White,Large,4.349670118537519,1,0,49,378,1,0 +1071,Dog,Golden Retriever,84,Brown,Large,10.457075642525009,1,0,25,435,0,0 +1072,Dog,Golden Retriever,82,Gray,Medium,7.392698661885805,1,0,74,145,0,1 +1073,Dog,Golden Retriever,107,Brown,Large,27.8333576490015,0,0,76,346,0,0 +1074,Dog,Poodle,21,Gray,Medium,26.171032618061805,1,0,41,465,0,1 +1075,Dog,Golden Retriever,178,Black,Medium,15.490813678204221,1,0,71,351,0,1 +1076,Rabbit,Rabbit,161,Brown,Medium,2.7789723600321725,1,0,64,57,1,1 +1077,Dog,Labrador,7,Orange,Large,21.72230803670153,1,0,61,176,0,1 +1078,Rabbit,Rabbit,70,Black,Medium,19.078940208751675,1,0,12,287,1,1 +1079,Dog,Poodle,127,Gray,Medium,29.146085451126382,1,0,81,244,0,1 +1080,Dog,Poodle,143,Black,Small,17.209643967840933,0,0,69,47,1,0 +1081,Cat,Persian,160,Brown,Medium,10.755417978684372,1,0,14,300,0,1 +1082,Dog,Labrador,148,White,Medium,16.05162911377812,1,0,34,437,0,1 +1083,Dog,Golden Retriever,143,White,Small,29.39863545073097,1,0,48,355,1,0 +1084,Dog,Poodle,176,Gray,Small,21.194510543457884,0,0,61,99,0,0 +1085,Dog,Golden Retriever,149,Gray,Large,20.487307999884482,0,0,81,212,0,0 +1086,Rabbit,Rabbit,170,White,Medium,7.979766102050676,1,0,35,301,0,1 +1087,Dog,Golden Retriever,154,Gray,Large,22.564473009257913,1,1,65,479,0,0 +1088,Bird,Parakeet,120,Black,Large,22.75788305468138,1,0,59,414,0,0 +1089,Rabbit,Rabbit,128,Orange,Small,10.201380529501254,0,1,36,164,1,0 +1090,Dog,Poodle,142,Orange,Large,15.027701139330683,0,0,79,399,0,0 +1091,Bird,Parakeet,57,Black,Medium,9.543496655273225,1,0,19,429,0,1 +1092,Dog,Labrador,165,Orange,Small,8.502447039404313,1,0,42,125,0,1 +1093,Cat,Siamese,58,Brown,Medium,1.7509860372350694,1,0,69,301,0,1 +1094,Bird,Parakeet,21,Black,Medium,9.271140594946631,1,0,19,461,0,1 +1095,Dog,Labrador,82,Gray,Small,17.06512854646997,1,0,39,120,1,1 +1096,Cat,Persian,69,Black,Medium,1.0464344389218359,0,0,69,87,1,0 +1097,Bird,Parakeet,131,White,Small,9.213353430480355,0,0,52,216,0,0 +1098,Rabbit,Rabbit,87,Gray,Medium,26.560899637374252,1,0,12,60,1,1 +1099,Bird,Parakeet,165,White,Small,18.215808968113464,1,0,83,301,0,0 +1100,Dog,Golden Retriever,49,Gray,Large,18.628021898348432,0,0,47,178,1,0 +1101,Rabbit,Rabbit,147,Orange,Medium,12.697564845764244,1,0,76,189,0,1 +1102,Cat,Persian,115,Black,Small,20.61769216127992,1,1,36,162,0,0 +1103,Rabbit,Rabbit,89,Brown,Medium,6.508050220680415,0,0,63,80,0,0 +1104,Rabbit,Rabbit,85,Gray,Medium,24.743657501141556,0,0,76,493,0,0 +1105,Cat,Persian,128,Gray,Medium,4.383984478141095,0,0,82,169,0,0 +1106,Rabbit,Rabbit,140,White,Small,20.02403928494017,1,1,51,300,0,0 +1107,Rabbit,Rabbit,169,Orange,Large,10.893330448770808,0,0,43,373,0,0 +1108,Dog,Poodle,161,White,Large,22.677876146891826,1,0,34,195,0,0 +1109,Rabbit,Rabbit,178,White,Large,16.94317198671458,1,0,49,202,0,0 +1110,Rabbit,Rabbit,117,Orange,Small,14.11823811588277,0,0,18,46,0,0 +1111,Cat,Persian,127,Brown,Large,5.913249660704163,0,0,61,462,0,0 +1112,Cat,Persian,71,White,Small,19.467822464246233,0,0,57,157,0,0 +1113,Cat,Persian,86,White,Medium,29.268325459191978,1,1,5,407,0,0 +1114,Rabbit,Rabbit,33,Gray,Medium,24.942499322303433,0,0,89,193,1,0 +1115,Dog,Labrador,81,Orange,Large,2.740237585081342,0,0,24,426,1,0 +1116,Rabbit,Rabbit,152,Black,Small,11.910651972474541,1,0,64,417,0,0 +1117,Rabbit,Rabbit,61,Brown,Medium,27.841907266517882,0,0,59,278,0,0 +1118,Bird,Parakeet,61,Brown,Small,15.588268679153622,1,0,38,316,0,0 +1119,Cat,Siamese,98,Orange,Small,20.201242948437198,1,0,33,478,1,0 +1120,Dog,Labrador,28,Gray,Medium,27.46538102215032,1,0,10,420,0,1 +1121,Dog,Labrador,43,Gray,Small,26.34004602217654,1,0,37,268,0,1 +1122,Rabbit,Rabbit,169,Orange,Medium,15.552790890674817,0,0,87,480,0,0 +1123,Dog,Labrador,167,Gray,Medium,13.69174277707046,1,0,11,198,0,1 +1124,Dog,Poodle,86,Brown,Medium,10.851085935908026,1,0,24,223,0,1 +1125,Bird,Parakeet,177,Brown,Small,21.851563851723842,1,0,88,262,0,0 +1126,Dog,Poodle,17,Black,Medium,9.322617184674755,0,1,71,425,0,0 +1127,Cat,Siamese,98,Orange,Small,8.246740586496104,1,0,21,165,0,0 +1128,Rabbit,Rabbit,98,White,Medium,11.331246943509623,0,0,20,179,0,0 +1129,Dog,Golden Retriever,65,White,Medium,6.139236649484968,1,1,10,427,0,0 +1130,Dog,Poodle,74,Black,Large,21.158474662427253,1,0,89,191,0,0 +1131,Cat,Persian,85,Black,Large,18.870978830587756,0,1,47,396,1,0 +1132,Cat,Persian,5,Gray,Large,15.003477489359463,1,0,47,215,0,1 +1133,Bird,Parakeet,2,Black,Small,12.319447598585086,1,0,15,283,0,1 +1134,Dog,Labrador,22,Black,Small,27.362813744005212,1,0,44,351,0,1 +1135,Bird,Parakeet,112,White,Medium,1.8445897646840532,1,1,62,106,0,0 +1136,Rabbit,Rabbit,57,Gray,Medium,23.304124561711674,1,0,6,465,0,1 +1137,Dog,Poodle,66,Black,Medium,27.155377580717772,1,1,66,97,0,0 +1138,Dog,Poodle,74,Black,Medium,13.944282803467853,0,0,76,367,0,0 +1139,Rabbit,Rabbit,98,Gray,Small,16.970024252648756,1,1,4,481,0,0 +1140,Bird,Parakeet,144,Orange,Medium,28.3894469485791,0,0,23,498,0,0 +1141,Dog,Golden Retriever,112,Gray,Medium,7.156047943279918,1,0,13,229,0,1 +1142,Dog,Poodle,179,White,Large,16.08391058575586,1,1,81,256,0,0 +1143,Bird,Parakeet,125,Orange,Medium,14.766912686718173,1,1,31,42,0,0 +1144,Dog,Poodle,167,Brown,Medium,8.493386788240732,1,0,16,241,0,1 +1145,Bird,Parakeet,86,Gray,Medium,24.73920368082013,1,0,41,41,0,1 +1146,Bird,Parakeet,76,White,Small,27.08841724813384,1,0,1,94,0,0 +1147,Dog,Labrador,88,Orange,Large,23.676044813858056,1,0,12,433,0,1 +1148,Dog,Poodle,168,Gray,Medium,6.665409864609643,1,0,56,253,0,1 +1149,Dog,Golden Retriever,38,Black,Medium,3.515710429540065,1,0,50,187,1,1 +1150,Bird,Parakeet,59,Gray,Large,18.557890073854928,1,0,20,384,1,0 +1151,Dog,Labrador,138,Brown,Medium,2.655748054786634,0,0,66,306,0,1 +1152,Rabbit,Rabbit,99,Gray,Small,20.51195076818444,1,0,36,399,0,0 +1153,Bird,Parakeet,63,Orange,Medium,16.382302389490036,1,0,79,166,0,1 +1154,Cat,Persian,49,Orange,Small,20.220786249566245,1,0,38,270,0,0 +1155,Dog,Labrador,32,Gray,Small,18.114952435842575,1,0,22,124,1,1 +1156,Bird,Parakeet,135,Orange,Medium,13.258374516288011,1,0,61,160,1,1 +1157,Dog,Poodle,21,Black,Large,6.82543822406662,1,0,50,86,1,1 +1158,Bird,Parakeet,20,Gray,Small,9.865361938504169,1,0,59,308,0,1 +1159,Dog,Golden Retriever,154,Black,Large,16.210516652350314,0,1,20,488,0,0 +1160,Dog,Labrador,127,Black,Medium,19.90417471578057,1,0,36,225,1,1 +1161,Dog,Poodle,68,White,Small,23.605007489651324,1,1,15,144,0,0 +1162,Cat,Siamese,110,Gray,Medium,23.247950835022763,0,0,62,344,1,0 +1163,Dog,Labrador,116,Orange,Large,29.207973554571474,1,0,3,247,0,1 +1164,Cat,Siamese,143,Black,Medium,9.289402011238874,1,0,5,361,0,1 +1165,Cat,Siamese,120,White,Medium,3.062210538189667,1,0,56,5,0,1 +1166,Bird,Parakeet,79,Gray,Small,11.02405113071392,1,0,31,30,0,0 +1167,Dog,Labrador,116,Gray,Medium,22.555539781498222,1,0,86,196,0,1 +1168,Rabbit,Rabbit,92,Brown,Medium,13.70523787996556,1,0,35,231,0,1 +1169,Cat,Siamese,14,Brown,Medium,24.715864487093164,1,0,71,295,0,1 +1170,Dog,Poodle,155,Black,Small,16.930464590394436,1,0,70,413,0,0 +1171,Rabbit,Rabbit,125,Brown,Large,27.963959956284587,1,0,26,324,0,0 +1172,Bird,Parakeet,168,Black,Large,28.488039817972997,1,0,1,459,0,0 +1173,Rabbit,Rabbit,58,Orange,Medium,3.0098306745437124,1,1,83,134,1,0 +1174,Bird,Parakeet,28,Black,Small,14.235989751660306,1,0,7,492,0,0 +1175,Cat,Persian,26,Brown,Medium,18.68098277623747,1,0,36,12,0,1 +1176,Cat,Persian,113,Orange,Large,8.42030368355417,1,0,76,496,1,0 +1177,Cat,Siamese,66,Brown,Small,12.805226601990427,1,1,84,3,1,0 +1178,Bird,Parakeet,153,Black,Large,20.65276469241325,1,0,87,177,0,0 +1179,Cat,Persian,131,White,Large,7.575292580052482,1,0,25,233,1,0 +1180,Dog,Poodle,44,Gray,Small,18.45745923625912,1,0,59,258,0,0 +1181,Rabbit,Rabbit,97,Brown,Small,21.225987016764876,1,0,61,139,0,0 +1182,Dog,Poodle,42,Black,Medium,3.624393021780136,1,0,29,64,0,1 +1183,Cat,Siamese,6,Orange,Medium,15.42835016479613,1,0,18,282,0,1 +1184,Dog,Labrador,167,Brown,Large,8.629320867801532,1,1,41,362,1,0 +1185,Rabbit,Rabbit,23,Brown,Small,22.00720432394329,0,0,19,305,1,0 +1186,Bird,Parakeet,89,Gray,Small,6.497109747571274,1,0,4,204,1,0 +1187,Bird,Parakeet,123,Black,Medium,26.874899550605086,1,0,65,16,1,1 +1188,Bird,Parakeet,178,White,Large,13.49729875903148,1,0,41,199,0,0 +1189,Bird,Parakeet,171,Orange,Large,6.742087878919052,1,0,15,31,0,0 +1190,Dog,Labrador,147,Black,Medium,13.70263209415955,1,0,54,211,1,1 +1191,Cat,Persian,51,Orange,Small,12.853569121447308,1,0,64,434,1,0 +1192,Bird,Parakeet,11,Brown,Medium,13.438649825317599,1,0,72,272,1,1 +1193,Bird,Parakeet,12,Gray,Medium,17.98127751142596,0,0,15,289,1,1 +1194,Bird,Parakeet,74,Gray,Medium,16.114038022492586,1,0,79,290,0,1 +1195,Cat,Persian,8,White,Medium,5.343536818746178,1,0,50,482,1,1 +1196,Rabbit,Rabbit,152,Gray,Small,2.901793106752453,1,0,22,485,1,0 +1197,Cat,Persian,41,Brown,Medium,22.153048013069675,0,0,18,233,1,0 +1198,Cat,Persian,164,White,Small,17.231805641921092,0,0,80,407,0,0 +1199,Cat,Persian,85,Black,Medium,11.411022274298649,1,0,2,181,0,1 +1200,Bird,Parakeet,72,Gray,Large,24.44237685244414,0,0,36,145,0,0 +1201,Bird,Parakeet,55,White,Small,12.253695753567753,0,0,20,49,0,0 +1202,Bird,Parakeet,137,Brown,Large,24.082987598417617,0,0,16,429,0,0 +1203,Bird,Parakeet,47,Orange,Medium,9.866927697858866,1,1,67,431,0,0 +1204,Cat,Persian,143,Orange,Medium,3.1125136288107926,1,0,30,102,0,1 +1205,Dog,Poodle,176,Brown,Medium,28.046551120197943,0,0,55,32,0,0 +1206,Dog,Golden Retriever,104,Brown,Small,12.441982749159822,0,0,66,496,0,0 +1207,Rabbit,Rabbit,42,Orange,Large,23.98808461263196,1,0,26,470,0,0 +1208,Cat,Siamese,60,White,Small,9.09517986344683,1,1,57,88,0,0 +1209,Cat,Siamese,12,Orange,Small,19.744588251586023,0,0,69,484,0,0 +1210,Dog,Labrador,130,Brown,Medium,26.32601532447412,1,0,2,410,0,1 +1211,Dog,Poodle,7,Gray,Large,2.2076766478594294,1,0,56,384,0,1 +1212,Rabbit,Rabbit,80,Black,Medium,25.088437175896587,1,1,84,340,0,0 +1213,Dog,Poodle,131,Gray,Small,17.56865714418593,1,0,62,314,0,0 +1214,Dog,Labrador,109,Gray,Medium,18.616680863348506,1,1,34,240,1,1 +1215,Cat,Persian,3,Brown,Large,16.171582023373496,1,0,56,265,0,1 +1216,Dog,Poodle,27,Orange,Medium,26.35977799522563,1,0,55,222,0,1 +1217,Dog,Poodle,114,White,Large,2.259567359833486,1,0,20,235,1,0 +1218,Bird,Parakeet,107,Gray,Large,8.569823354539595,1,1,50,152,0,0 +1219,Bird,Parakeet,40,Orange,Medium,26.397113626915505,1,1,25,467,1,0 +1220,Rabbit,Rabbit,161,White,Small,22.818025373426725,1,0,84,56,1,0 +1221,Bird,Parakeet,129,Gray,Medium,1.5117096451795695,1,0,25,321,0,1 +1222,Bird,Parakeet,8,White,Small,15.993182213081552,0,0,53,374,0,0 +1223,Bird,Parakeet,101,Orange,Small,16.357607740667568,1,0,14,111,0,0 +1224,Dog,Golden Retriever,160,Black,Medium,12.6255819187094,0,0,60,14,0,0 +1225,Bird,Parakeet,124,Orange,Small,9.6244066297977,0,0,43,411,0,0 +1226,Rabbit,Rabbit,176,Brown,Small,20.770020661957922,0,0,62,268,0,0 +1227,Bird,Parakeet,107,White,Medium,7.02592347643786,1,0,76,113,1,1 +1228,Bird,Parakeet,101,Black,Medium,24.169508300079475,0,0,69,251,0,0 +1229,Cat,Persian,4,White,Medium,24.288077662256683,1,0,63,499,0,1 +1230,Rabbit,Rabbit,110,White,Small,13.042581689611795,1,1,2,373,0,0 +1231,Rabbit,Rabbit,19,Gray,Large,9.222892316379783,1,0,54,141,0,1 +1232,Rabbit,Rabbit,32,Brown,Small,9.232467266130683,1,0,16,113,0,0 +1233,Rabbit,Rabbit,137,White,Large,14.986448395890093,1,0,55,449,0,0 +1234,Rabbit,Rabbit,12,Orange,Medium,21.867797517731404,1,0,43,289,0,1 +1235,Dog,Golden Retriever,139,Orange,Large,11.407192038821904,1,0,61,482,1,0 +1236,Rabbit,Rabbit,46,Gray,Medium,18.691257018274793,1,0,38,356,0,1 +1237,Bird,Parakeet,119,Brown,Medium,12.092049356895236,1,0,45,24,0,1 +1238,Bird,Parakeet,16,Brown,Large,9.831172485390116,1,1,51,367,0,0 +1239,Dog,Poodle,129,Orange,Medium,1.7209259615580308,1,0,42,309,0,1 +1240,Dog,Poodle,63,Black,Large,1.1135090663263285,0,0,24,254,0,0 +1241,Bird,Parakeet,109,Gray,Medium,7.58902265712813,1,1,30,462,1,0 +1242,Bird,Parakeet,119,Gray,Small,12.785782317228868,1,0,37,267,1,0 +1243,Dog,Labrador,21,Orange,Small,25.68657385349975,1,0,53,125,0,1 +1244,Cat,Persian,158,Orange,Medium,24.156043044982336,1,1,63,392,0,0 +1245,Cat,Siamese,13,Black,Medium,8.747792615051708,1,0,11,249,0,1 +1246,Cat,Siamese,162,Black,Large,8.09167284654468,1,0,78,286,0,0 +1247,Cat,Siamese,4,Brown,Small,7.592340061030796,0,1,23,333,0,0 +1248,Dog,Labrador,156,Gray,Large,10.837882875337144,0,0,80,476,0,0 +1249,Rabbit,Rabbit,102,Gray,Medium,15.447762867288876,0,1,50,52,0,0 +1250,Cat,Persian,59,Orange,Medium,13.72136538891569,1,0,85,367,0,1 +1251,Cat,Siamese,128,Brown,Large,7.329922764663319,1,0,55,42,1,0 +1252,Cat,Persian,27,Gray,Medium,4.783090888364923,1,0,55,165,1,1 +1253,Dog,Golden Retriever,69,Brown,Small,1.3647565620996662,1,1,4,41,0,0 +1254,Dog,Poodle,25,Brown,Large,5.464933921328612,1,0,54,379,0,0 +1255,Dog,Golden Retriever,131,Orange,Small,21.455334735629428,1,0,61,455,1,0 +1256,Dog,Poodle,97,Brown,Small,8.065518511021905,1,0,78,175,0,0 +1257,Bird,Parakeet,25,Black,Small,14.589452493523785,1,0,83,66,1,0 +1258,Rabbit,Rabbit,148,Black,Large,18.147733318774097,1,0,5,65,0,0 +1259,Cat,Siamese,162,White,Medium,26.718386182116607,0,1,18,399,0,0 +1260,Bird,Parakeet,154,Orange,Small,26.776797853409164,1,0,44,82,0,0 +1261,Rabbit,Rabbit,126,White,Small,22.62627827675941,1,0,31,349,1,0 +1262,Dog,Labrador,133,Black,Small,2.019552769406541,0,0,21,104,0,0 +1263,Cat,Persian,9,White,Medium,17.677042153477032,1,0,47,399,0,1 +1264,Dog,Labrador,116,Orange,Medium,23.810920654113687,1,0,30,286,0,1 +1265,Bird,Parakeet,178,Orange,Large,1.501353298137533,1,0,55,373,1,0 +1266,Dog,Golden Retriever,134,Brown,Medium,16.94085379604077,0,0,68,185,1,0 +1267,Bird,Parakeet,60,Gray,Large,25.875160227794854,1,0,69,283,1,0 +1268,Bird,Parakeet,172,Brown,Large,14.748960199425742,1,0,41,3,1,0 +1269,Rabbit,Rabbit,121,Orange,Large,1.119079884026005,0,0,45,137,0,0 +1270,Cat,Persian,139,Black,Large,1.3193873917326164,1,0,74,178,0,0 +1271,Cat,Persian,175,Black,Large,13.082445084998765,0,1,80,236,0,0 +1272,Bird,Parakeet,108,Brown,Small,5.495251355085882,0,0,30,17,0,0 +1273,Cat,Persian,108,Orange,Medium,20.269069055704527,0,0,77,302,1,0 +1274,Cat,Siamese,131,Black,Small,21.555178682278466,0,0,85,415,1,0 +1275,Cat,Persian,76,Orange,Large,6.15851406835823,1,0,45,413,0,0 +1276,Bird,Parakeet,30,Black,Medium,26.76746536721006,0,0,45,419,0,0 +1277,Rabbit,Rabbit,21,Orange,Small,13.81532750438885,1,0,79,8,1,1 +1278,Bird,Parakeet,156,White,Small,5.142531320165788,1,0,84,419,0,0 +1279,Bird,Parakeet,161,Orange,Large,23.16329634401247,1,0,84,28,0,0 +1280,Bird,Parakeet,163,Orange,Small,23.90926050048641,1,0,45,143,0,0 +1281,Bird,Parakeet,34,Brown,Small,19.53956870729827,1,0,55,347,0,0 +1282,Bird,Parakeet,41,White,Small,25.750501042874042,1,0,59,148,0,0 +1283,Cat,Siamese,51,Gray,Small,22.38381618888947,0,0,5,63,0,0 +1284,Bird,Parakeet,175,Brown,Small,26.583966379582726,1,0,32,366,0,0 +1285,Rabbit,Rabbit,82,Gray,Large,13.582753406078904,1,1,6,449,1,0 +1286,Dog,Poodle,58,Black,Large,22.68031671488383,1,1,26,379,0,0 +1287,Cat,Persian,75,Gray,Small,29.992795339442285,0,0,37,186,0,0 +1288,Bird,Parakeet,23,Orange,Large,22.02300472806896,1,0,13,365,1,1 +1289,Bird,Parakeet,176,Gray,Large,4.311329626876833,1,0,69,226,0,0 +1290,Bird,Parakeet,128,Brown,Medium,16.031242950446305,1,0,25,117,0,1 +1291,Rabbit,Rabbit,150,Brown,Small,18.03308594604781,1,0,13,411,1,0 +1292,Dog,Golden Retriever,17,Gray,Large,6.052109577070339,0,1,88,438,0,0 +1293,Dog,Poodle,69,Brown,Large,28.92385739116309,1,0,20,355,1,0 +1294,Bird,Parakeet,108,Brown,Small,6.091813551403873,0,1,72,253,0,0 +1295,Dog,Labrador,139,Gray,Medium,6.14917967168346,0,0,60,30,0,1 +1296,Bird,Parakeet,94,White,Small,27.539847001262093,1,0,73,160,0,0 +1297,Cat,Persian,15,White,Medium,14.49095756052846,0,0,21,214,0,1 +1298,Cat,Persian,91,Black,Small,2.83246009896097,0,1,61,443,1,0 +1299,Dog,Labrador,163,Brown,Small,18.347437498217328,0,0,84,168,0,0 +1300,Dog,Labrador,33,Gray,Small,18.452964110358387,1,0,79,80,1,1 +1301,Rabbit,Rabbit,125,Brown,Small,2.870522001910292,1,1,26,45,1,0 +1302,Cat,Siamese,16,Orange,Medium,29.2031523733762,1,0,54,48,0,1 +1303,Dog,Labrador,162,Brown,Medium,16.776868993542692,1,0,42,448,1,1 +1304,Cat,Siamese,66,Brown,Large,17.234075943213163,0,0,11,297,0,0 +1305,Rabbit,Rabbit,168,White,Small,16.08334702056944,1,0,37,406,0,0 +1306,Bird,Parakeet,156,Gray,Medium,18.71122160969398,1,0,11,367,1,1 +1307,Cat,Persian,47,Brown,Medium,8.664692830438893,1,0,27,282,0,1 +1308,Bird,Parakeet,167,White,Medium,9.329999091270384,0,0,83,472,0,0 +1309,Rabbit,Rabbit,148,Black,Small,15.82883360408785,0,0,34,415,0,0 +1310,Rabbit,Rabbit,124,White,Large,24.393889788143806,1,0,38,466,0,0 +1311,Dog,Golden Retriever,1,White,Medium,2.446072718331187,1,0,61,87,0,1 +1312,Cat,Siamese,87,White,Small,18.498722154387135,1,1,87,120,0,0 +1313,Dog,Poodle,128,Brown,Medium,24.396566132683105,1,1,53,2,0,0 +1314,Rabbit,Rabbit,67,Brown,Medium,5.550600889079588,0,0,65,315,0,0 +1315,Cat,Persian,4,Black,Large,1.4356537174364226,1,0,3,459,0,1 +1316,Cat,Siamese,153,Orange,Medium,10.068685559102285,1,0,67,226,0,1 +1317,Rabbit,Rabbit,47,Orange,Medium,18.056167495158345,0,0,28,78,0,0 +1318,Bird,Parakeet,38,Orange,Small,5.0102120287376835,0,0,14,144,1,0 +1319,Dog,Poodle,143,Black,Small,13.35754875820208,0,0,52,241,0,0 +1320,Dog,Labrador,21,Orange,Large,17.01732342385406,0,0,30,31,0,1 +1321,Dog,Labrador,57,Orange,Small,18.3676097479622,1,0,58,262,0,1 +1322,Cat,Siamese,117,White,Small,8.957939909010879,1,0,54,210,0,0 +1323,Cat,Persian,46,Brown,Large,12.60085944360245,1,0,69,70,0,0 +1324,Bird,Parakeet,142,Brown,Medium,23.571837642959203,1,0,15,383,1,1 +1325,Rabbit,Rabbit,145,White,Large,28.29522612949988,1,0,38,196,1,0 +1326,Rabbit,Rabbit,39,White,Small,25.99452960242946,1,0,78,57,1,0 +1327,Cat,Siamese,137,White,Large,17.364221550269527,0,1,14,423,1,0 +1328,Cat,Persian,99,Brown,Medium,12.89849128537445,1,0,57,468,0,1 +1329,Cat,Siamese,77,Orange,Large,10.655741816771128,1,0,59,111,0,0 +1330,Rabbit,Rabbit,115,White,Small,4.623740416436524,0,0,8,47,1,0 +1331,Bird,Parakeet,134,Black,Small,4.089986169267276,1,0,72,434,0,0 +1332,Dog,Golden Retriever,151,White,Large,20.962371122496073,1,0,19,184,1,0 +1333,Bird,Parakeet,91,Gray,Medium,8.534038068963651,1,0,32,366,1,1 +1334,Bird,Parakeet,98,White,Large,8.385188906540957,0,0,29,326,1,0 +1335,Bird,Parakeet,120,Brown,Medium,29.947656579855785,1,0,41,164,0,1 +1336,Rabbit,Rabbit,46,White,Medium,6.449852778340067,1,0,75,214,1,1 +1337,Dog,Golden Retriever,38,Gray,Large,5.29373302517606,0,0,45,330,0,0 +1338,Cat,Persian,142,Black,Medium,3.861871605037698,0,0,13,329,0,0 +1339,Rabbit,Rabbit,100,Orange,Small,17.81435500983709,1,1,27,120,0,0 +1340,Rabbit,Rabbit,72,White,Large,19.20805095406094,0,0,19,212,1,0 +1341,Bird,Parakeet,35,Gray,Medium,11.591609861449577,1,0,26,310,1,1 +1342,Rabbit,Rabbit,147,Gray,Small,16.526986729832565,1,0,53,40,0,0 +1343,Bird,Parakeet,30,Black,Large,23.863586397402464,0,0,51,352,0,0 +1344,Cat,Persian,78,Brown,Small,24.725020881330703,0,0,37,403,0,0 +1345,Dog,Poodle,84,White,Small,28.7794501957538,1,0,9,288,1,0 +1346,Rabbit,Rabbit,113,Gray,Small,24.903771591763363,0,0,52,167,0,0 +1347,Cat,Siamese,25,Black,Medium,17.380401664805373,0,0,54,482,0,0 +1348,Cat,Siamese,168,Orange,Large,6.210052009268534,1,1,3,33,0,0 +1349,Cat,Siamese,88,Gray,Large,3.8931431733824295,1,0,69,317,0,0 +1350,Bird,Parakeet,14,Black,Medium,2.580844807414917,1,0,8,483,0,1 +1351,Rabbit,Rabbit,154,Brown,Large,20.32924912177613,0,0,14,474,0,0 +1352,Cat,Siamese,102,Brown,Small,24.418551406541802,1,0,3,274,1,0 +1353,Dog,Labrador,150,Gray,Large,15.751834569838291,1,0,24,426,1,1 +1354,Cat,Persian,176,Gray,Medium,2.586711070175558,1,1,13,196,0,0 +1355,Cat,Persian,83,White,Small,16.528646663148805,1,0,43,190,1,0 +1356,Bird,Parakeet,110,Black,Medium,28.136728584211962,0,0,29,220,0,0 +1357,Rabbit,Rabbit,179,Gray,Small,18.330782537041355,1,1,72,9,0,0 +1358,Dog,Labrador,131,White,Small,24.47339716542163,1,0,51,119,0,1 +1359,Rabbit,Rabbit,170,White,Large,24.204426803360768,1,0,42,122,0,0 +1360,Bird,Parakeet,19,Gray,Small,28.031743893638932,1,0,31,246,1,1 +1361,Dog,Poodle,28,Brown,Medium,23.336634203846327,1,0,63,359,0,1 +1362,Cat,Persian,173,Black,Large,22.683314906379596,0,0,26,396,0,0 +1363,Cat,Siamese,71,Black,Medium,7.788977284148577,1,0,18,220,1,1 +1364,Cat,Siamese,63,Brown,Small,2.737829504311362,0,0,82,260,0,0 +1365,Bird,Parakeet,73,Gray,Medium,16.010384486560426,1,0,57,57,1,1 +1366,Rabbit,Rabbit,124,Black,Medium,4.608592030455901,1,0,7,314,1,1 +1367,Dog,Golden Retriever,119,Gray,Small,28.903680412061746,1,0,48,459,0,0 +1368,Bird,Parakeet,121,Gray,Large,9.407818116357829,0,0,80,127,1,0 +1369,Dog,Poodle,19,White,Medium,25.7353818981445,1,0,60,186,0,1 +1370,Cat,Persian,175,Brown,Small,12.24111413087132,1,0,33,204,0,0 +1371,Cat,Siamese,142,Gray,Large,10.372633245863945,1,0,3,85,0,0 +1372,Bird,Parakeet,120,Orange,Large,28.119598598922014,0,0,4,103,0,0 +1373,Cat,Persian,166,Brown,Large,11.536354590500496,1,1,4,142,1,0 +1374,Cat,Siamese,61,Black,Medium,12.318349351230957,0,0,42,204,0,0 +1375,Rabbit,Rabbit,164,White,Medium,4.076661608774366,0,0,9,128,0,0 +1376,Cat,Persian,168,Black,Medium,20.44295329319277,0,1,51,245,1,0 +1377,Dog,Golden Retriever,30,Black,Large,9.37634301984789,0,0,17,37,0,0 +1378,Dog,Labrador,9,Brown,Medium,17.98252086734571,1,0,17,362,0,1 +1379,Dog,Labrador,64,Black,Small,18.149199417543073,1,0,20,108,0,1 +1380,Bird,Parakeet,161,Gray,Medium,21.856791172277244,1,0,14,308,1,1 +1381,Bird,Parakeet,120,Black,Medium,22.612003099000795,0,0,22,51,0,0 +1382,Rabbit,Rabbit,52,White,Large,15.919120597644643,1,0,52,0,0,0 +1383,Cat,Siamese,89,White,Medium,10.598766480498687,1,0,27,10,0,1 +1384,Bird,Parakeet,142,Orange,Small,25.286075950207284,0,0,43,313,0,0 +1385,Cat,Siamese,53,White,Large,9.986103832846057,1,0,3,234,1,0 +1386,Dog,Labrador,78,Black,Small,15.717763739610612,1,0,88,127,0,1 +1387,Cat,Persian,41,White,Large,12.636324912908114,1,0,8,423,0,0 +1388,Rabbit,Rabbit,68,Brown,Large,25.797273193120954,1,0,86,213,0,0 +1389,Cat,Persian,8,White,Medium,13.525637403229036,1,1,89,482,1,1 +1390,Dog,Golden Retriever,2,Gray,Small,12.196876476287045,0,0,10,443,1,0 +1391,Dog,Poodle,105,Brown,Large,21.004887094031485,1,0,29,242,0,0 +1392,Rabbit,Rabbit,125,Orange,Medium,18.47079218598184,0,0,40,43,0,0 +1393,Bird,Parakeet,110,Gray,Large,4.1101581713795365,1,1,65,5,0,0 +1394,Bird,Parakeet,86,Black,Large,15.736895383974934,1,0,21,155,0,0 +1395,Dog,Poodle,169,Black,Large,22.32887326166418,1,0,24,414,0,0 +1396,Rabbit,Rabbit,120,Black,Large,29.77016817314933,1,1,73,7,0,0 +1397,Rabbit,Rabbit,127,Gray,Large,19.245258185139253,1,1,60,4,0,0 +1398,Dog,Golden Retriever,16,Gray,Large,11.738498926798215,0,0,26,361,1,0 +1399,Dog,Labrador,62,Gray,Medium,7.558396394261434,1,0,9,181,0,1 +1400,Cat,Persian,113,Brown,Large,26.312076790446287,1,0,20,111,1,0 +1401,Cat,Siamese,176,Brown,Small,24.62239115129158,1,1,56,491,0,0 +1402,Dog,Labrador,158,Black,Medium,7.783761802602161,1,0,36,272,1,1 +1403,Dog,Golden Retriever,12,White,Small,6.81833061425063,0,1,36,421,0,0 +1404,Bird,Parakeet,35,Black,Large,21.73896652752409,0,0,73,264,0,0 +1405,Cat,Persian,11,Orange,Small,10.133609795644025,1,0,22,457,1,1 +1406,Cat,Persian,64,Brown,Large,9.885565152496778,1,0,1,211,0,0 +1407,Dog,Labrador,50,Gray,Large,5.416417164962002,0,0,69,77,0,0 +1408,Cat,Siamese,149,Brown,Medium,8.655290071585068,1,0,73,269,0,1 +1409,Cat,Persian,69,Gray,Medium,13.397719170186837,1,0,37,153,1,1 +1410,Rabbit,Rabbit,48,Orange,Large,22.25231178090005,0,0,83,220,0,0 +1411,Bird,Parakeet,20,Orange,Medium,10.752934737998743,1,0,72,368,1,1 +1412,Rabbit,Rabbit,101,Brown,Small,10.529525844915439,1,0,13,445,0,0 +1413,Cat,Persian,172,Gray,Medium,9.262484324020638,1,0,84,346,0,1 +1414,Bird,Parakeet,161,Orange,Medium,14.870226357820247,1,0,43,24,0,1 +1415,Dog,Poodle,137,Black,Small,6.2384642402981445,1,0,37,463,0,0 +1416,Dog,Golden Retriever,68,Black,Medium,15.08329128693164,1,0,74,419,0,1 +1417,Bird,Parakeet,35,Gray,Large,21.421091129646005,0,1,25,353,0,0 +1418,Bird,Parakeet,65,Brown,Small,1.863924616946043,0,0,88,207,0,0 +1419,Dog,Labrador,8,Orange,Large,1.2836020288670942,1,0,21,416,0,1 +1420,Dog,Labrador,45,Brown,Small,3.1785643944807678,1,0,41,2,0,1 +1421,Bird,Parakeet,87,Black,Large,16.53414179011525,0,0,73,214,1,0 +1422,Bird,Parakeet,175,Orange,Large,25.7791974123767,1,0,42,394,1,0 +1423,Cat,Persian,168,Brown,Medium,1.1416495145873062,0,1,42,111,1,0 +1424,Dog,Golden Retriever,76,White,Large,28.16317783295575,1,1,2,441,0,0 +1425,Bird,Parakeet,76,Gray,Medium,4.082076835545538,0,0,68,191,1,0 +1426,Bird,Parakeet,174,Gray,Small,25.41752231368856,1,0,8,401,0,0 +1427,Cat,Persian,47,White,Large,15.002788184505457,1,0,61,46,0,0 +1428,Bird,Parakeet,15,Black,Large,20.121364231086474,0,0,14,488,1,0 +1429,Bird,Parakeet,105,Orange,Small,15.80980654896829,1,0,77,427,1,0 +1430,Bird,Parakeet,147,Gray,Small,14.834398530201284,1,0,21,220,0,0 +1431,Rabbit,Rabbit,26,White,Large,25.212083422567353,1,0,24,186,0,0 +1432,Cat,Persian,52,White,Small,22.920705007466672,0,1,50,49,0,0 +1433,Rabbit,Rabbit,93,Black,Medium,15.01866485916712,1,0,82,196,0,1 +1434,Bird,Parakeet,150,Black,Small,4.237441952485787,0,0,45,215,1,0 +1435,Bird,Parakeet,21,Brown,Small,4.346685820117836,1,0,64,412,0,1 +1436,Dog,Labrador,51,Gray,Large,24.244644360853187,1,1,64,220,1,0 +1437,Rabbit,Rabbit,38,Brown,Medium,16.35232801907847,1,0,26,379,0,1 +1438,Cat,Siamese,20,White,Small,14.966085066361515,1,0,32,89,1,1 +1439,Bird,Parakeet,157,Orange,Medium,20.736438399460738,0,0,57,231,1,0 +1440,Rabbit,Rabbit,179,Orange,Medium,25.906405919013547,0,0,53,322,0,0 +1441,Dog,Poodle,132,Black,Medium,19.237321187985852,1,0,33,257,0,1 +1442,Dog,Labrador,55,Orange,Large,23.12258916804789,0,0,52,152,1,0 +1443,Dog,Labrador,92,Orange,Small,23.19284519939175,0,0,18,255,0,0 +1444,Bird,Parakeet,28,Orange,Large,11.331582014995814,1,0,35,402,0,0 +1445,Bird,Parakeet,4,White,Small,8.948412673588876,0,1,12,423,0,0 +1446,Dog,Poodle,150,Orange,Small,24.2337579480796,1,0,51,257,0,0 +1447,Rabbit,Rabbit,76,Orange,Large,11.938184961743062,0,0,89,466,1,0 +1448,Cat,Persian,173,Orange,Medium,27.19674418915845,1,0,10,64,0,1 +1449,Cat,Siamese,67,Brown,Medium,3.021030205173469,1,0,52,189,1,1 +1450,Bird,Parakeet,86,White,Small,2.6883078501848496,1,0,34,90,0,0 +1451,Cat,Siamese,162,Black,Large,27.883120642940842,0,1,67,437,1,0 +1452,Cat,Persian,91,Brown,Medium,19.805192511261613,1,1,39,172,0,0 +1453,Dog,Golden Retriever,105,Gray,Medium,12.008381337001637,1,0,8,246,0,1 +1454,Bird,Parakeet,1,White,Small,22.8967520296693,1,0,29,255,0,1 +1455,Rabbit,Rabbit,72,White,Small,20.497477489815598,1,0,22,115,1,0 +1456,Dog,Golden Retriever,87,Gray,Large,16.40483035776829,1,0,84,48,0,0 +1457,Dog,Labrador,166,Gray,Small,3.8673269372168892,0,0,70,226,1,0 +1458,Dog,Golden Retriever,6,Gray,Medium,24.553208031489795,1,0,61,488,0,1 +1459,Rabbit,Rabbit,153,Black,Medium,26.114672529738115,1,1,43,68,0,0 +1460,Rabbit,Rabbit,145,Orange,Medium,5.992891968026824,1,0,49,421,1,1 +1461,Dog,Poodle,61,White,Medium,25.664837907334935,0,0,29,387,0,0 +1462,Bird,Parakeet,12,Orange,Large,26.457572019480363,1,1,81,400,0,0 +1463,Bird,Parakeet,6,Orange,Large,16.933429756366962,1,0,31,125,0,1 +1464,Bird,Parakeet,27,Gray,Small,20.029192050457922,0,0,53,436,0,0 +1465,Bird,Parakeet,136,Black,Small,12.084882280420251,0,0,34,212,0,0 +1466,Rabbit,Rabbit,124,Brown,Small,9.171957777768714,0,0,38,52,0,0 +1467,Rabbit,Rabbit,106,Black,Large,21.9375112615977,1,1,57,111,0,0 +1468,Cat,Siamese,93,Orange,Medium,25.99437932145844,0,1,4,158,1,0 +1469,Bird,Parakeet,109,Black,Small,19.968249035716678,1,1,2,239,1,0 +1470,Dog,Golden Retriever,99,Gray,Medium,15.836127708749927,1,1,12,149,0,0 +1471,Cat,Persian,168,Brown,Small,5.608818238480975,1,0,35,138,0,0 +1472,Rabbit,Rabbit,142,Gray,Small,2.5503026655397267,1,1,70,417,0,0 +1473,Cat,Persian,35,Brown,Small,13.925061917155647,0,1,72,112,1,0 +1474,Rabbit,Rabbit,176,Orange,Small,2.431137347225217,1,0,21,142,0,0 +1475,Bird,Parakeet,84,Brown,Small,17.51070432530953,1,0,61,369,0,0 +1476,Bird,Parakeet,56,Brown,Medium,12.099146548215368,0,0,35,137,1,0 +1477,Cat,Persian,177,Brown,Small,22.958484470777115,0,0,48,485,0,0 +1478,Bird,Parakeet,141,Black,Medium,9.86380640824932,1,0,15,479,1,1 +1479,Rabbit,Rabbit,49,Black,Large,18.19649832805553,1,1,71,436,0,0 +1480,Cat,Siamese,46,Orange,Small,16.592822130897915,1,1,15,303,0,0 +1481,Rabbit,Rabbit,113,Brown,Medium,1.1366603401449935,0,0,6,30,1,0 +1482,Cat,Persian,136,White,Large,3.099292549665,1,0,81,292,0,0 +1483,Dog,Labrador,43,Orange,Large,9.420678162094594,0,1,68,107,1,0 +1484,Bird,Parakeet,148,Brown,Large,26.584817349602186,1,0,22,283,0,0 +1485,Rabbit,Rabbit,124,Brown,Medium,5.263433692093578,1,0,32,158,1,1 +1486,Rabbit,Rabbit,97,Black,Small,21.615935583355707,1,0,22,325,0,0 +1487,Bird,Parakeet,90,Orange,Small,29.227630418416332,0,1,27,13,0,0 +1488,Rabbit,Rabbit,39,Brown,Medium,23.9264590280435,1,1,15,121,0,0 +1489,Rabbit,Rabbit,85,Brown,Small,8.045415488924274,0,0,44,214,0,0 +1490,Rabbit,Rabbit,177,Orange,Medium,27.34259255197937,1,0,86,271,0,1 +1491,Rabbit,Rabbit,62,White,Small,3.700107538743312,1,1,9,24,0,0 +1492,Rabbit,Rabbit,106,Orange,Large,14.576116314251337,0,0,34,422,0,0 +1493,Dog,Labrador,107,Orange,Small,26.738715324066,1,0,73,321,0,1 +1494,Rabbit,Rabbit,54,White,Medium,19.596705568449224,1,0,15,293,0,1 +1495,Bird,Parakeet,126,Orange,Medium,12.995692377103266,1,0,62,168,0,1 +1496,Cat,Siamese,9,Brown,Large,9.491573135377894,1,0,45,102,0,1 +1497,Rabbit,Rabbit,88,Orange,Medium,11.877882754232148,1,0,84,353,1,1 +1498,Bird,Parakeet,37,White,Small,15.7593116492693,1,1,72,370,0,0 +1499,Cat,Siamese,49,Gray,Small,10.252273966311414,0,0,58,39,0,0 +1500,Cat,Siamese,41,Orange,Large,29.100068000386692,1,1,19,234,0,0 +1501,Dog,Poodle,17,Black,Medium,28.05925193891821,1,1,16,319,1,1 +1502,Rabbit,Rabbit,88,Brown,Large,26.2494725782153,1,0,75,88,0,0 +1503,Dog,Poodle,21,Brown,Small,13.23306523015695,1,0,8,99,0,1 +1504,Bird,Parakeet,51,Black,Large,21.047751310721637,1,0,33,143,0,0 +1505,Bird,Parakeet,59,Black,Medium,27.182049904042973,0,0,74,81,0,0 +1506,Dog,Poodle,167,Orange,Large,25.662455142688913,0,0,18,76,1,0 +1507,Dog,Labrador,41,Black,Medium,13.015483406586021,1,0,35,58,0,1 +1508,Cat,Persian,39,Gray,Medium,4.25919543034556,1,0,43,228,0,1 +1509,Rabbit,Rabbit,141,White,Small,3.1932805512019344,1,0,50,90,1,0 +1510,Cat,Siamese,11,Black,Small,17.734857311545376,1,1,3,353,0,0 +1511,Dog,Labrador,147,Black,Small,19.636210121117017,0,0,59,394,1,0 +1512,Bird,Parakeet,139,Gray,Medium,25.645435533313698,1,0,28,0,0,1 +1513,Cat,Siamese,77,Brown,Large,9.4829355898899,1,0,46,268,1,0 +1514,Bird,Parakeet,127,Brown,Medium,5.856644225951232,1,1,64,285,1,0 +1515,Rabbit,Rabbit,46,Orange,Small,11.991169162167784,1,0,70,196,0,0 +1516,Bird,Parakeet,45,Gray,Small,11.253678585335976,1,0,47,273,1,0 +1517,Dog,Golden Retriever,1,White,Large,4.841599666517787,1,0,14,77,0,1 +1518,Dog,Labrador,168,White,Small,20.49619692288141,1,0,7,140,0,1 +1519,Dog,Poodle,108,Gray,Small,15.752066281151903,1,1,74,287,0,0 +1520,Rabbit,Rabbit,19,White,Small,21.91255992732822,1,0,79,70,0,1 +1521,Dog,Labrador,76,Brown,Large,11.228042484922305,1,0,31,455,1,1 +1522,Cat,Siamese,72,White,Medium,10.445364135753367,1,0,81,49,0,1 +1523,Bird,Parakeet,109,White,Small,14.704699738271813,1,1,51,454,1,0 +1524,Bird,Parakeet,12,Gray,Large,26.444813016459264,1,0,29,371,0,1 +1525,Rabbit,Rabbit,161,Gray,Small,2.1681578952854297,1,1,15,368,0,0 +1526,Dog,Poodle,45,White,Large,24.34015170159769,0,0,76,79,0,0 +1527,Bird,Parakeet,90,Black,Small,11.700640767074939,1,0,25,234,0,0 +1528,Cat,Persian,103,Gray,Large,25.783951747865853,1,0,61,286,1,0 +1529,Bird,Parakeet,8,Orange,Small,24.263887256239837,1,0,41,306,1,1 +1530,Cat,Siamese,153,Orange,Medium,11.114076174486563,1,0,69,154,1,1 +1531,Cat,Siamese,91,Brown,Small,21.12241952432686,0,1,4,59,1,0 +1532,Dog,Golden Retriever,174,White,Large,5.629349946429764,0,0,57,32,0,0 +1533,Bird,Parakeet,87,Black,Small,26.99216501274985,0,1,31,436,0,0 +1534,Rabbit,Rabbit,158,Brown,Medium,12.730240869105373,1,0,3,126,0,1 +1535,Bird,Parakeet,175,White,Small,14.213142845300805,1,0,67,220,0,0 +1536,Bird,Parakeet,177,Gray,Large,21.46701248007256,1,1,50,199,0,0 +1537,Cat,Persian,175,Orange,Large,24.804366864440677,1,0,17,416,0,0 +1538,Dog,Golden Retriever,84,Black,Medium,7.853939556518078,1,0,60,125,0,1 +1539,Rabbit,Rabbit,82,Orange,Large,20.179154056432868,0,1,74,40,0,0 +1540,Cat,Siamese,146,Gray,Small,12.367943272785306,0,0,66,274,1,0 +1541,Rabbit,Rabbit,68,White,Small,11.692384796114878,1,0,88,468,0,0 +1542,Dog,Labrador,161,Gray,Small,21.010748894278986,1,1,75,296,0,0 +1543,Dog,Golden Retriever,127,Gray,Large,12.228314950522096,1,0,24,84,0,0 +1544,Bird,Parakeet,164,Orange,Small,11.037146504767822,1,1,87,88,1,0 +1545,Cat,Persian,82,Black,Small,25.24076383566103,1,0,32,41,1,0 +1546,Rabbit,Rabbit,147,Gray,Small,3.9370075910991846,0,0,87,119,0,0 +1547,Cat,Siamese,156,Orange,Medium,20.60088606207611,1,0,19,278,0,1 +1548,Dog,Golden Retriever,168,Black,Large,8.213036419928114,1,0,66,458,1,0 +1549,Dog,Labrador,84,White,Large,1.4494490129595188,1,0,60,32,0,1 +1550,Dog,Poodle,113,White,Large,5.8361637973080684,0,1,66,275,1,0 +1551,Cat,Persian,76,Brown,Medium,18.593184363500164,1,0,15,152,1,1 +1552,Bird,Parakeet,122,Orange,Large,21.564688553092186,1,0,85,89,0,0 +1553,Dog,Labrador,143,Black,Medium,1.6106757231332813,0,0,35,103,0,1 +1554,Bird,Parakeet,41,Gray,Medium,2.0563378557498844,1,0,69,435,0,1 +1555,Dog,Labrador,167,White,Large,27.17372133044564,0,0,27,11,0,0 +1556,Cat,Siamese,2,Black,Small,3.125868725297875,1,0,88,159,1,1 +1557,Bird,Parakeet,29,Orange,Small,26.931282599880568,1,0,7,416,1,0 +1558,Bird,Parakeet,105,Brown,Large,7.616811498776268,1,0,6,457,1,0 +1559,Rabbit,Rabbit,51,White,Large,22.46051620458817,1,0,33,195,0,0 +1560,Bird,Parakeet,75,Black,Medium,9.98226421943435,0,0,58,398,0,0 +1561,Cat,Persian,10,Black,Large,25.90380874266633,0,0,49,306,0,0 +1562,Cat,Siamese,142,Black,Medium,21.31319109730638,0,0,17,261,0,0 +1563,Dog,Labrador,51,Orange,Large,12.652590299449159,0,0,42,90,0,0 +1564,Dog,Poodle,118,Orange,Small,3.212771171048307,1,1,72,196,0,0 +1565,Rabbit,Rabbit,121,Orange,Small,22.248305332253405,0,1,40,68,0,0 +1566,Rabbit,Rabbit,39,Gray,Large,18.889657922990263,1,0,46,465,1,0 +1567,Bird,Parakeet,28,Black,Medium,1.0844398171798735,1,0,71,219,0,1 +1568,Cat,Siamese,3,White,Small,1.2731758232009143,1,1,70,418,1,0 +1569,Cat,Siamese,84,Orange,Medium,24.454996222588125,1,0,68,378,0,1 +1570,Bird,Parakeet,155,Orange,Large,8.522592425253762,1,0,1,213,0,0 +1571,Cat,Persian,117,Black,Large,19.447876364113906,1,0,79,417,1,0 +1572,Cat,Persian,2,Orange,Small,14.172218173192176,0,0,89,446,1,0 +1573,Dog,Poodle,93,Gray,Large,26.039470421131252,1,0,52,451,0,0 +1574,Cat,Persian,166,Gray,Small,27.59187227997217,1,0,35,145,0,0 +1575,Bird,Parakeet,131,Orange,Medium,29.907423667686768,1,0,69,232,0,1 +1576,Bird,Parakeet,144,Black,Small,5.456839938410158,1,0,5,370,0,0 +1577,Rabbit,Rabbit,94,Black,Small,26.025957347788786,1,1,17,409,1,0 +1578,Dog,Labrador,40,Black,Medium,8.714619925135251,1,0,53,233,0,1 +1579,Bird,Parakeet,2,Brown,Small,18.13366037196972,1,0,52,213,0,1 +1580,Bird,Parakeet,81,Orange,Small,15.667412182207116,1,0,14,366,1,0 +1581,Bird,Parakeet,169,Black,Large,1.521655815599242,1,0,29,480,0,0 +1582,Rabbit,Rabbit,12,Gray,Medium,28.05036711727675,1,0,8,60,0,1 +1583,Cat,Persian,163,White,Small,3.271484599327335,0,0,1,362,0,0 +1584,Bird,Parakeet,65,White,Medium,20.95834866282434,1,0,29,77,0,1 +1585,Cat,Persian,55,Black,Medium,23.7651982500396,1,0,36,94,0,1 +1586,Rabbit,Rabbit,45,White,Small,24.063347622382842,0,0,51,329,0,0 +1587,Dog,Golden Retriever,101,White,Large,4.929341048936498,1,0,37,498,0,0 +1588,Bird,Parakeet,62,Brown,Medium,13.979528564731192,1,0,69,215,1,1 +1589,Rabbit,Rabbit,49,Gray,Small,25.053437242585414,1,0,53,212,1,0 +1590,Rabbit,Rabbit,127,Gray,Medium,9.777697100983886,0,0,52,303,0,0 +1591,Cat,Siamese,153,Brown,Large,14.564711585865243,1,0,88,199,0,0 +1592,Dog,Labrador,23,Brown,Large,8.911022888295527,1,0,83,351,0,1 +1593,Dog,Golden Retriever,152,White,Large,14.442991118674401,1,0,12,30,0,0 +1594,Bird,Parakeet,38,Orange,Small,26.747299755193524,1,0,52,289,1,0 +1595,Rabbit,Rabbit,28,White,Small,14.198602699950314,1,0,1,342,0,0 +1596,Bird,Parakeet,21,Brown,Large,17.121448495155292,0,1,7,472,0,0 +1597,Bird,Parakeet,107,Gray,Small,12.715312215789423,1,1,17,134,0,0 +1598,Bird,Parakeet,146,White,Large,14.34339831289644,0,0,43,82,0,0 +1599,Dog,Poodle,164,Orange,Small,1.4400517723462292,1,0,71,390,0,0 +1600,Dog,Golden Retriever,99,White,Large,9.228619788853225,1,0,17,46,1,0 +1601,Dog,Golden Retriever,173,Black,Medium,13.660869930512908,1,0,59,391,1,1 +1602,Bird,Parakeet,119,White,Large,19.380112303789137,0,0,3,205,1,0 +1603,Dog,Golden Retriever,73,Brown,Large,14.051505986062873,1,0,29,384,1,0 +1604,Rabbit,Rabbit,122,Gray,Small,10.796694256325358,0,0,22,380,1,0 +1605,Cat,Siamese,30,Gray,Small,12.254863324980558,1,0,38,76,0,0 +1606,Rabbit,Rabbit,108,Brown,Small,8.026506546813529,1,0,54,258,0,0 +1607,Rabbit,Rabbit,98,Black,Small,27.810334200447535,1,0,50,436,1,0 +1608,Cat,Persian,32,Brown,Small,16.130553547936522,1,0,69,91,0,0 +1609,Rabbit,Rabbit,156,White,Small,1.3888741622915703,0,0,30,421,0,0 +1610,Cat,Siamese,172,Brown,Medium,1.5639414941668517,1,0,23,434,0,1 +1611,Rabbit,Rabbit,79,Brown,Large,29.215887413609206,1,0,85,398,0,0 +1612,Rabbit,Rabbit,41,Black,Large,7.422075849824106,1,0,47,99,0,0 +1613,Rabbit,Rabbit,63,White,Large,24.362640375615126,1,0,21,382,0,0 +1614,Dog,Poodle,23,Orange,Medium,2.6899837452566597,1,0,20,194,0,1 +1615,Dog,Poodle,104,Orange,Large,27.713719364141923,1,0,23,191,1,0 +1616,Bird,Parakeet,75,Gray,Medium,26.358093106333673,1,0,68,67,0,1 +1617,Bird,Parakeet,48,Brown,Medium,17.561701710600925,1,0,21,388,1,1 +1618,Rabbit,Rabbit,160,Gray,Medium,1.2609610612144175,1,0,21,241,0,1 +1619,Cat,Siamese,57,Black,Large,8.47651534667679,1,0,1,89,0,0 +1620,Cat,Siamese,118,Orange,Medium,12.797856774555484,0,1,15,344,0,0 +1621,Dog,Golden Retriever,100,Brown,Large,14.34185938567929,1,0,17,9,0,0 +1622,Dog,Golden Retriever,124,Black,Large,9.632683998308789,0,0,51,263,0,0 +1623,Rabbit,Rabbit,161,Black,Small,1.9554881791203693,0,0,63,267,1,0 +1624,Dog,Golden Retriever,152,Brown,Small,18.11369477186972,1,1,14,319,1,0 +1625,Rabbit,Rabbit,167,Black,Medium,16.200485363147923,1,0,59,117,1,1 +1626,Dog,Poodle,34,Brown,Medium,9.207018427443254,0,0,83,249,0,0 +1627,Cat,Siamese,156,Brown,Large,2.1013030595185893,1,0,50,58,0,0 +1628,Bird,Parakeet,165,White,Medium,29.843231429436266,1,1,34,194,0,0 +1629,Dog,Golden Retriever,7,Gray,Medium,14.977599417965772,1,0,6,474,0,1 +1630,Cat,Siamese,165,Black,Large,22.924123011448238,1,0,49,49,0,0 +1631,Cat,Siamese,56,Black,Medium,14.11710701648873,1,0,6,406,0,1 +1632,Bird,Parakeet,65,White,Medium,2.251938435025302,1,0,69,295,0,1 +1633,Bird,Parakeet,47,White,Large,9.978421607479502,0,1,7,377,0,0 +1634,Rabbit,Rabbit,148,White,Large,10.283194089171134,0,0,77,96,0,0 +1635,Rabbit,Rabbit,19,Orange,Small,25.312234217421462,0,0,51,461,0,0 +1636,Dog,Labrador,157,Orange,Large,19.972669361996356,1,1,1,126,0,0 +1637,Cat,Persian,146,Orange,Small,8.987467205544712,1,1,13,245,1,0 +1638,Cat,Siamese,163,Brown,Medium,29.064458685113493,1,0,43,86,0,1 +1639,Cat,Siamese,171,Black,Large,10.32471313953827,1,0,27,478,0,0 +1640,Rabbit,Rabbit,126,Gray,Large,29.94878674903289,1,1,71,76,0,0 +1641,Bird,Parakeet,139,Brown,Large,29.888747101937632,0,0,3,376,0,0 +1642,Rabbit,Rabbit,49,Black,Medium,6.545105014655512,1,1,58,203,1,0 +1643,Cat,Persian,113,Black,Small,10.069492369463605,1,1,89,482,1,0 +1644,Rabbit,Rabbit,38,White,Medium,15.095281756054971,0,0,17,145,0,0 +1645,Rabbit,Rabbit,64,White,Medium,20.258919463288837,0,1,45,148,0,0 +1646,Dog,Poodle,69,Brown,Large,16.31337602111018,1,0,78,492,1,0 +1647,Cat,Persian,136,White,Small,29.11380260483545,1,0,58,110,0,0 +1648,Bird,Parakeet,162,Black,Medium,21.995685762835052,1,0,56,489,1,1 +1649,Cat,Siamese,176,White,Large,7.149300632106258,1,1,54,128,0,0 +1650,Rabbit,Rabbit,116,Orange,Medium,4.047828956195511,1,0,87,252,0,1 +1651,Rabbit,Rabbit,65,White,Medium,20.963637430571346,0,0,88,165,1,0 +1652,Dog,Poodle,31,Black,Large,5.595120765242127,1,1,29,296,0,0 +1653,Bird,Parakeet,132,Brown,Large,23.84262836973687,1,0,54,425,0,0 +1654,Dog,Poodle,113,Gray,Small,10.020063350549773,1,0,27,494,0,0 +1655,Dog,Poodle,109,White,Medium,10.479904628612589,1,1,45,136,0,0 +1656,Dog,Labrador,178,Black,Large,1.313153379295847,1,0,87,278,1,1 +1657,Bird,Parakeet,69,Gray,Large,21.32932674575618,1,1,31,333,0,0 +1658,Dog,Poodle,158,Gray,Small,4.284822838875888,1,1,36,10,0,0 +1659,Cat,Siamese,120,Orange,Small,23.413536746442027,1,0,81,70,1,0 +1660,Cat,Persian,88,Gray,Large,15.264166835598997,1,0,61,158,0,0 +1661,Cat,Persian,67,Black,Small,23.744114633682504,0,0,37,215,0,0 +1662,Dog,Labrador,75,Black,Large,17.933869636734496,1,0,67,25,1,1 +1663,Cat,Siamese,45,White,Medium,3.390564888550732,1,0,36,285,1,1 +1664,Dog,Golden Retriever,4,White,Small,23.20391776445031,1,0,5,161,0,1 +1665,Cat,Persian,154,White,Medium,3.0241850619477426,0,0,46,429,0,0 +1666,Rabbit,Rabbit,155,Gray,Medium,14.882349896510636,1,0,79,101,1,1 +1667,Dog,Poodle,174,Gray,Medium,5.998127210203935,1,0,52,68,1,1 +1668,Rabbit,Rabbit,107,Orange,Large,22.817720161358665,1,0,49,132,1,0 +1669,Dog,Poodle,51,White,Medium,27.43614560637767,1,0,6,188,0,1 +1670,Cat,Siamese,50,Brown,Medium,17.069658737389954,1,0,10,210,0,1 +1671,Dog,Labrador,6,Orange,Medium,16.287271534676236,1,0,23,242,0,1 +1672,Cat,Persian,59,Brown,Large,23.15493269344292,1,0,17,165,1,0 +1673,Rabbit,Rabbit,125,Gray,Medium,1.1472855762960892,1,0,84,411,1,1 +1674,Bird,Parakeet,143,Orange,Medium,27.615000636419662,0,1,44,409,0,0 +1675,Dog,Poodle,30,Orange,Small,6.72601569564282,0,0,41,60,0,0 +1676,Cat,Siamese,170,Black,Large,29.53944763096088,1,0,15,361,0,0 +1677,Bird,Parakeet,122,Gray,Medium,22.351364490853403,1,0,44,278,1,1 +1678,Dog,Golden Retriever,125,Gray,Small,23.45328328331946,1,0,58,429,0,0 +1679,Cat,Siamese,37,Orange,Large,20.02988643083459,0,0,86,112,0,0 +1680,Rabbit,Rabbit,170,Gray,Large,14.678232300769649,1,0,50,29,0,0 +1681,Cat,Persian,124,Orange,Large,17.5772031074738,1,0,52,322,0,0 +1682,Dog,Labrador,101,Black,Large,2.345683694438644,0,0,70,475,0,0 +1683,Dog,Golden Retriever,35,Orange,Large,19.094825773528413,1,1,68,160,1,0 +1684,Cat,Siamese,87,Gray,Large,19.156844957624354,1,0,63,233,1,0 +1685,Dog,Poodle,134,Gray,Medium,26.844823124652475,1,0,58,386,0,1 +1686,Dog,Poodle,18,Orange,Medium,15.664034365476363,1,1,23,452,0,1 +1687,Cat,Persian,113,Orange,Medium,3.237186848495769,1,0,44,376,1,1 +1688,Rabbit,Rabbit,29,Orange,Medium,9.879681693980356,0,0,83,338,0,0 +1689,Cat,Persian,77,White,Small,20.013252951315206,1,0,44,169,0,0 +1690,Cat,Siamese,59,Gray,Medium,7.938507019792453,0,0,52,317,1,0 +1691,Dog,Golden Retriever,175,Gray,Large,9.135306833806627,1,0,67,185,1,0 +1692,Dog,Golden Retriever,126,White,Medium,24.79360147446475,0,0,28,56,1,0 +1693,Cat,Siamese,145,Orange,Medium,17.94527222111856,1,0,67,239,0,1 +1694,Dog,Golden Retriever,109,Black,Large,15.747295521433603,1,0,78,256,0,0 +1695,Rabbit,Rabbit,22,Orange,Medium,10.235107037368326,0,0,18,357,0,1 +1696,Bird,Parakeet,68,Orange,Medium,22.196913042057968,1,0,27,200,0,1 +1697,Rabbit,Rabbit,69,White,Medium,9.343834600210851,0,0,51,443,1,0 +1698,Rabbit,Rabbit,165,Gray,Small,22.374842011681082,1,0,60,324,0,0 +1699,Dog,Golden Retriever,49,Orange,Medium,3.032942927755818,1,0,74,314,1,1 +1700,Bird,Parakeet,141,Gray,Large,26.071698905880424,0,0,49,301,0,0 +1701,Rabbit,Rabbit,162,Brown,Large,15.487427437902051,0,0,15,105,0,0 +1702,Bird,Parakeet,109,Gray,Medium,13.71673248427013,1,0,83,289,1,1 +1703,Cat,Persian,86,Black,Medium,18.719678934715812,0,0,73,100,0,0 +1704,Bird,Parakeet,53,Black,Medium,19.101570113991844,1,0,68,96,0,1 +1705,Bird,Parakeet,123,Orange,Large,4.829020129809871,1,0,15,152,0,0 +1706,Rabbit,Rabbit,87,Gray,Large,22.930774104670185,1,0,51,425,0,0 +1707,Cat,Persian,179,Orange,Small,9.001920640551825,1,0,48,28,0,0 +1708,Dog,Labrador,131,White,Small,27.243894524927505,1,0,72,152,0,1 +1709,Cat,Persian,42,Black,Small,24.71766923903014,1,0,1,437,0,0 +1710,Bird,Parakeet,98,Orange,Large,11.041715204507707,0,0,7,378,1,0 +1711,Rabbit,Rabbit,160,White,Small,24.424439502820718,0,0,53,72,0,0 +1712,Bird,Parakeet,109,Orange,Medium,11.33532447969722,0,1,31,253,0,0 +1713,Bird,Parakeet,51,Gray,Medium,3.927482767445178,0,0,18,450,0,0 +1714,Rabbit,Rabbit,85,Gray,Large,28.89475892304553,0,0,12,398,0,0 +1715,Bird,Parakeet,174,Gray,Small,19.925458441247763,1,1,48,3,0,0 +1716,Dog,Poodle,127,Gray,Medium,14.22688627321516,1,0,30,236,0,1 +1717,Bird,Parakeet,21,Brown,Large,12.53542719440015,1,0,7,37,0,1 +1718,Dog,Golden Retriever,44,White,Small,10.416454693489655,0,0,3,19,1,0 +1719,Cat,Siamese,139,White,Medium,29.46198026744475,1,0,63,112,1,1 +1720,Cat,Persian,103,Gray,Small,4.907670688796315,1,0,6,22,1,0 +1721,Dog,Labrador,68,Brown,Large,10.129346708696481,1,0,15,420,0,1 +1722,Dog,Labrador,129,Gray,Small,15.079431568197242,1,0,71,445,0,1 +1723,Dog,Labrador,92,Brown,Large,14.86198680657503,1,0,43,366,1,1 +1724,Dog,Golden Retriever,24,Gray,Small,14.66962804782746,1,0,81,135,0,0 +1725,Rabbit,Rabbit,125,Black,Medium,6.097730659081073,1,0,18,420,1,1 +1726,Cat,Siamese,141,Black,Large,5.203043286175516,0,1,22,247,0,0 +1727,Bird,Parakeet,111,Orange,Large,8.388260003529982,1,0,61,300,0,0 +1728,Bird,Parakeet,98,Gray,Large,27.521258460259624,1,1,61,96,1,0 +1729,Bird,Parakeet,139,White,Small,4.278536067200793,0,0,53,32,1,0 +1730,Cat,Siamese,179,Orange,Small,22.96532799782642,1,0,26,134,1,0 +1731,Rabbit,Rabbit,17,Black,Medium,22.968903400867767,1,0,40,254,1,1 +1732,Rabbit,Rabbit,99,Gray,Large,24.483341713020607,1,0,88,277,0,0 +1733,Bird,Parakeet,82,Orange,Large,26.71184918069131,0,1,8,132,0,0 +1734,Cat,Persian,104,Gray,Small,8.628049148628978,1,1,21,237,1,0 +1735,Cat,Persian,152,Black,Small,7.512224608797995,1,0,43,14,0,0 +1736,Bird,Parakeet,19,Orange,Medium,14.499664329296913,1,0,21,276,0,1 +1737,Dog,Golden Retriever,165,Black,Medium,8.687585893809048,0,0,3,125,0,0 +1738,Bird,Parakeet,85,Brown,Small,22.294989680510117,1,1,31,344,0,0 +1739,Dog,Labrador,8,Black,Large,19.442247725501577,1,0,27,16,0,1 +1740,Dog,Poodle,28,Gray,Medium,26.374642953526894,1,0,27,311,0,1 +1741,Dog,Golden Retriever,5,White,Small,3.399410801125075,0,0,73,49,1,0 +1742,Dog,Golden Retriever,145,Black,Small,27.537416667384168,1,0,57,134,1,0 +1743,Rabbit,Rabbit,101,White,Large,9.507110173432597,1,1,33,117,0,0 +1744,Cat,Persian,164,Brown,Small,15.230136031882537,0,0,44,377,0,0 +1745,Rabbit,Rabbit,118,Orange,Small,13.719731642249666,1,0,43,305,0,0 +1746,Bird,Parakeet,147,Orange,Medium,23.049037882028294,1,0,31,59,0,1 +1747,Rabbit,Rabbit,64,White,Small,13.189665973523434,1,0,65,124,0,0 +1748,Bird,Parakeet,91,Orange,Small,28.363041223832873,1,0,84,466,0,0 +1749,Dog,Labrador,117,Black,Medium,27.17348259651869,0,0,76,115,0,1 +1750,Bird,Parakeet,98,White,Large,26.29267340711569,1,0,17,390,0,0 +1751,Dog,Poodle,11,Gray,Large,3.066491646798609,1,1,15,423,0,0 +1752,Rabbit,Rabbit,54,Black,Medium,25.773483785064244,1,0,34,172,0,1 +1753,Cat,Persian,173,Black,Medium,23.667427917853104,1,0,80,186,0,1 +1754,Dog,Poodle,47,Black,Large,4.001875463177799,0,0,4,497,0,0 +1755,Dog,Golden Retriever,140,Gray,Large,4.115015437650435,1,0,72,87,0,0 +1756,Bird,Parakeet,149,Brown,Large,3.933750534008694,1,0,89,202,1,0 +1757,Cat,Siamese,139,Orange,Large,1.3286452029301388,1,1,79,360,0,0 +1758,Dog,Poodle,5,White,Large,16.910394850642923,1,0,28,310,1,1 +1759,Bird,Parakeet,20,Brown,Medium,29.228434451645256,1,0,73,319,0,1 +1760,Rabbit,Rabbit,169,White,Small,13.721856384094012,0,0,2,448,1,0 +1761,Cat,Persian,46,Orange,Large,16.681967554733333,0,0,17,181,0,0 +1762,Bird,Parakeet,12,Gray,Large,29.07112410632871,0,0,58,351,1,0 +1763,Dog,Golden Retriever,70,Black,Small,8.40028463445269,1,0,74,167,0,0 +1764,Cat,Siamese,17,Black,Small,25.895637325349625,0,0,9,180,1,0 +1765,Cat,Persian,115,Gray,Small,16.845323549140346,0,0,31,299,0,0 +1766,Bird,Parakeet,147,Brown,Small,17.51481892522731,1,0,66,446,0,0 +1767,Cat,Siamese,154,Brown,Small,10.952041859011223,0,0,88,129,1,0 +1768,Dog,Golden Retriever,48,White,Small,17.285946468040336,1,0,62,462,0,0 +1769,Dog,Golden Retriever,108,Gray,Medium,3.121132774870234,1,0,19,148,1,1 +1770,Rabbit,Rabbit,52,Brown,Small,23.174663605809254,0,0,81,471,0,0 +1771,Rabbit,Rabbit,96,Gray,Small,9.255945084783878,1,1,74,305,0,0 +1772,Cat,Siamese,70,Orange,Medium,26.740641555324693,0,0,55,208,1,0 +1773,Bird,Parakeet,102,Black,Small,11.667670143616295,1,0,28,110,0,0 +1774,Dog,Poodle,75,Black,Large,5.1418992041195954,1,0,50,435,0,0 +1775,Bird,Parakeet,161,Black,Large,21.180565656686063,0,1,65,499,1,0 +1776,Cat,Persian,151,Gray,Small,5.772766196691583,0,1,3,136,1,0 +1777,Bird,Parakeet,143,Gray,Medium,22.856734950746723,1,0,79,354,1,1 +1778,Bird,Parakeet,155,White,Large,29.07525485577738,0,0,52,403,0,0 +1779,Cat,Siamese,179,Black,Medium,23.60703319506259,0,1,10,210,0,0 +1780,Cat,Siamese,82,White,Large,1.6510282045084157,1,0,21,145,0,0 +1781,Dog,Poodle,92,Black,Small,18.396687853530466,1,0,37,83,0,0 +1782,Bird,Parakeet,179,White,Medium,21.166909458122007,1,1,15,29,1,0 +1783,Rabbit,Rabbit,175,White,Medium,24.938291448092272,1,0,87,487,0,1 +1784,Bird,Parakeet,4,Orange,Medium,2.6240669592310044,1,0,78,64,0,1 +1785,Rabbit,Rabbit,135,White,Large,14.923127010616303,0,1,79,23,0,0 +1786,Rabbit,Rabbit,109,Gray,Large,16.525755306513435,1,0,74,471,1,0 +1787,Dog,Poodle,163,White,Large,5.904621016681163,1,0,9,430,1,0 +1788,Bird,Parakeet,48,Black,Small,10.35104448160884,1,0,44,210,0,0 +1789,Dog,Golden Retriever,73,White,Large,13.76643006142879,1,0,39,221,0,0 +1790,Rabbit,Rabbit,72,Brown,Small,25.163080950776244,0,0,5,89,1,0 +1791,Cat,Siamese,116,Black,Medium,27.952819416036,0,0,25,105,0,0 +1792,Bird,Parakeet,173,Brown,Medium,19.224262038612512,1,0,56,171,0,1 +1793,Rabbit,Rabbit,81,Black,Small,12.453410394622402,1,1,64,229,0,0 +1794,Bird,Parakeet,81,Gray,Large,8.366899516170593,1,0,72,412,0,0 +1795,Bird,Parakeet,113,Gray,Small,29.481920348648362,0,0,86,76,0,0 +1796,Rabbit,Rabbit,96,Gray,Medium,29.507579944214395,1,0,52,67,1,1 +1797,Dog,Labrador,72,Brown,Medium,27.824331498816008,1,0,89,364,0,1 +1798,Cat,Siamese,147,Orange,Small,23.784405346456758,1,0,76,400,0,0 +1799,Dog,Poodle,63,Black,Large,10.44760676862293,1,1,9,352,1,0 +1800,Dog,Labrador,38,Gray,Large,24.69301402143129,1,0,10,388,1,1 +1801,Cat,Persian,67,Black,Small,12.854997500864004,0,0,10,91,0,0 +1802,Cat,Persian,69,Brown,Small,10.47641984649631,1,0,73,87,0,0 +1803,Bird,Parakeet,13,Orange,Large,22.60109539552211,1,0,62,464,0,1 +1804,Bird,Parakeet,175,Brown,Medium,3.3099639009922637,0,0,60,49,0,0 +1805,Cat,Persian,31,Black,Small,2.408318975574189,0,0,19,102,0,0 +1806,Cat,Siamese,114,Orange,Large,13.990657218254382,1,0,38,367,0,0 +1807,Bird,Parakeet,22,Orange,Large,13.923525223842695,1,1,6,423,0,0 +1808,Dog,Labrador,93,Brown,Medium,26.973680438851215,1,0,15,491,0,1 +1809,Cat,Persian,171,Gray,Medium,4.658439792346687,0,0,47,45,0,0 +1810,Rabbit,Rabbit,67,Black,Medium,4.575233896119971,1,0,45,178,0,1 +1811,Bird,Parakeet,54,White,Medium,5.276125413772133,1,0,5,266,0,1 +1812,Bird,Parakeet,138,White,Medium,1.8623515775434756,1,0,78,267,0,1 +1813,Bird,Parakeet,42,Brown,Small,3.080220396632343,1,0,53,396,0,0 +1814,Cat,Persian,64,Black,Large,20.136226138042133,1,0,66,319,0,0 +1815,Dog,Poodle,126,Black,Small,13.515945544407767,1,1,62,7,0,0 +1816,Dog,Labrador,21,Brown,Small,23.144488945552396,1,0,23,448,0,1 +1817,Cat,Siamese,169,Black,Small,29.785333838868148,1,0,44,264,0,0 +1818,Rabbit,Rabbit,152,Gray,Small,20.11200575187104,1,1,52,475,1,0 +1819,Dog,Golden Retriever,35,Brown,Small,2.4632115479835885,1,0,46,385,1,0 +1820,Dog,Poodle,30,Gray,Medium,4.032323077875904,1,0,19,77,0,1 +1821,Cat,Siamese,57,Black,Medium,26.549118459626527,1,1,88,396,1,0 +1822,Bird,Parakeet,47,Brown,Large,16.297382392189583,1,0,84,239,0,0 +1823,Bird,Parakeet,163,Black,Small,24.92027762247646,1,0,47,257,0,0 +1824,Rabbit,Rabbit,19,Brown,Large,25.841757750522856,1,0,52,195,1,1 +1825,Dog,Labrador,114,Black,Medium,27.902561703486057,1,0,24,459,0,1 +1826,Cat,Siamese,8,Gray,Medium,28.545046959104607,1,0,17,431,0,1 +1827,Bird,Parakeet,26,Gray,Medium,17.08994809859588,1,0,89,329,0,1 +1828,Dog,Poodle,113,Black,Small,10.889584995424983,0,0,46,130,0,0 +1829,Rabbit,Rabbit,71,Brown,Small,28.89408135609196,1,0,64,451,0,0 +1830,Cat,Persian,30,White,Medium,16.87478577403553,0,0,28,389,0,0 +1831,Rabbit,Rabbit,135,Gray,Large,2.86332896568372,0,1,50,342,0,0 +1832,Cat,Persian,67,Gray,Large,19.640519951736085,1,1,64,260,0,0 +1833,Dog,Labrador,65,White,Medium,18.042857761455313,0,0,28,241,0,1 +1834,Rabbit,Rabbit,36,Black,Large,26.977993313946474,1,1,86,386,1,0 +1835,Dog,Labrador,25,Brown,Medium,19.942428485576563,0,0,56,393,1,1 +1836,Cat,Siamese,37,Black,Large,11.12252030938499,0,0,59,170,0,0 +1837,Dog,Golden Retriever,1,Orange,Medium,29.02054604546879,1,0,5,146,0,1 +1838,Dog,Labrador,143,Gray,Large,24.246965093478973,1,0,54,269,1,1 +1839,Rabbit,Rabbit,37,Orange,Small,19.282110933059275,1,1,44,148,0,0 +1840,Rabbit,Rabbit,141,Gray,Medium,9.494830898690564,1,0,42,379,0,1 +1841,Rabbit,Rabbit,136,Gray,Medium,11.83266859311457,0,0,72,298,0,0 +1842,Bird,Parakeet,133,Orange,Large,20.429127798803044,1,0,57,27,1,0 +1843,Cat,Siamese,128,Orange,Medium,3.08744373543633,1,0,47,148,0,1 +1844,Dog,Labrador,127,Black,Medium,8.44373715785764,1,0,76,419,0,1 +1845,Bird,Parakeet,1,Orange,Medium,21.70312706850134,1,1,25,279,0,1 +1846,Rabbit,Rabbit,164,Black,Small,14.920536785494006,1,0,44,181,0,0 +1847,Cat,Siamese,35,Black,Small,12.669413379618474,1,0,15,165,0,0 +1848,Rabbit,Rabbit,104,Black,Large,5.39227104961202,1,0,51,16,0,0 +1849,Bird,Parakeet,23,Gray,Small,13.813621999854245,1,0,53,149,0,1 +1850,Cat,Persian,9,Gray,Large,23.350067152046783,1,0,34,338,0,1 +1851,Bird,Parakeet,34,Orange,Small,8.656372536084898,0,0,77,44,0,0 +1852,Cat,Siamese,174,Gray,Large,1.156557723326645,1,0,8,329,1,0 +1853,Cat,Siamese,128,Orange,Small,20.3866421333785,1,0,77,188,1,0 +1854,Bird,Parakeet,4,Gray,Small,29.206171528544026,1,1,70,138,0,0 +1855,Rabbit,Rabbit,158,Brown,Large,18.597970344068905,1,0,21,60,0,0 +1856,Rabbit,Rabbit,102,Brown,Medium,4.4777339088867745,0,0,7,293,0,0 +1857,Bird,Parakeet,75,Orange,Medium,10.30125967799552,1,1,79,321,0,0 +1858,Rabbit,Rabbit,99,Gray,Small,26.161606826886477,1,0,56,32,1,0 +1859,Rabbit,Rabbit,72,Gray,Large,7.744474153448029,0,0,49,298,0,0 +1860,Dog,Golden Retriever,3,Black,Medium,18.623181201430633,1,0,42,414,0,1 +1861,Cat,Siamese,142,White,Large,12.677465039365938,1,0,23,111,1,0 +1862,Bird,Parakeet,173,Orange,Medium,7.027467921169822,1,0,52,290,1,1 +1863,Dog,Poodle,34,White,Large,24.58879991337571,1,0,79,36,0,0 +1864,Cat,Siamese,45,White,Small,22.542107017505018,1,1,53,430,0,0 +1865,Cat,Siamese,61,Orange,Large,20.186140912818697,0,1,16,494,0,0 +1866,Cat,Siamese,83,Gray,Medium,7.853761117793785,1,0,37,99,1,1 +1867,Dog,Poodle,123,Orange,Small,3.837853078777051,1,0,18,22,1,0 +1868,Bird,Parakeet,102,White,Large,17.70862056495302,0,1,20,419,0,0 +1869,Rabbit,Rabbit,16,Gray,Medium,17.05665717264357,1,0,46,423,0,1 +1870,Cat,Siamese,66,Brown,Small,6.463165270711317,1,0,2,486,0,0 +1871,Dog,Poodle,168,Orange,Large,2.631154135140002,1,0,87,217,0,0 +1872,Bird,Parakeet,145,Orange,Medium,3.3405451716255934,1,1,63,88,0,0 +1873,Bird,Parakeet,119,Gray,Large,29.79035860886928,1,0,3,208,0,0 +1874,Dog,Labrador,162,Brown,Large,23.723932947543627,1,0,46,270,0,1 +1875,Bird,Parakeet,124,Brown,Small,27.737636802990007,1,0,49,420,1,0 +1876,Rabbit,Rabbit,144,Gray,Small,6.656586944854537,0,1,1,236,0,0 +1877,Cat,Siamese,102,Black,Medium,28.85648755337962,1,0,22,387,0,1 +1878,Dog,Golden Retriever,66,Orange,Small,5.136976192721701,1,0,14,405,0,0 +1879,Cat,Siamese,13,Orange,Medium,15.763373028427662,1,0,85,208,1,1 +1880,Cat,Persian,111,White,Large,25.061174848094854,1,0,45,277,0,0 +1881,Cat,Siamese,159,Gray,Large,11.144297280835803,1,0,6,464,1,0 +1882,Rabbit,Rabbit,34,White,Medium,13.320027462130115,1,1,65,205,0,0 +1883,Bird,Parakeet,39,White,Large,4.717368198673838,0,0,21,77,1,0 +1884,Rabbit,Rabbit,136,Brown,Small,4.56473451997021,1,0,4,400,1,0 +1885,Bird,Parakeet,120,Orange,Large,18.967478518581128,0,0,3,327,0,0 +1886,Cat,Siamese,126,White,Small,20.590438689981646,1,0,30,97,1,0 +1887,Rabbit,Rabbit,55,Brown,Medium,23.542642908383606,1,0,31,345,0,1 +1888,Dog,Golden Retriever,27,Gray,Medium,17.143779527770853,1,0,34,364,0,1 +1889,Dog,Labrador,118,White,Large,19.727019475577872,0,1,83,349,0,0 +1890,Rabbit,Rabbit,53,Orange,Large,1.3920008792918817,1,0,20,469,0,0 +1891,Cat,Siamese,39,White,Small,5.702951086978453,0,0,87,335,1,0 +1892,Cat,Siamese,65,Gray,Small,29.562528214490236,0,0,54,93,1,0 +1893,Dog,Golden Retriever,145,White,Large,3.203811683159122,1,0,25,79,0,0 +1894,Bird,Parakeet,158,Orange,Large,20.684858445547103,1,0,15,37,0,0 +1895,Rabbit,Rabbit,13,Brown,Medium,6.10639635791977,0,0,38,407,0,1 +1896,Dog,Labrador,21,Orange,Medium,21.784838987261615,0,0,23,227,0,1 +1897,Bird,Parakeet,147,Brown,Medium,21.47372168589781,1,0,52,482,0,1 +1898,Dog,Poodle,53,Black,Large,7.791772733854615,1,0,66,133,1,0 +1899,Rabbit,Rabbit,19,Orange,Large,8.81939757735692,0,0,57,195,0,0 +1900,Cat,Siamese,57,Orange,Large,19.98835069446056,1,0,57,465,0,0 +1901,Bird,Parakeet,25,Gray,Medium,12.312495124626196,1,1,79,389,1,0 +1902,Rabbit,Rabbit,31,White,Large,6.641891121297427,1,0,52,281,0,0 +1903,Dog,Labrador,81,Brown,Small,22.21870836483697,1,1,35,100,0,0 +1904,Bird,Parakeet,23,Brown,Large,5.470278205753691,0,0,74,474,0,0 +1905,Bird,Parakeet,32,Gray,Large,25.81366626020345,1,0,14,424,0,0 +1906,Cat,Persian,74,Gray,Large,15.925416197502999,1,0,14,74,1,0 +1907,Rabbit,Rabbit,10,White,Large,4.06531751249972,1,0,33,293,0,1 +1908,Bird,Parakeet,54,White,Large,21.128097672398546,1,0,52,207,0,0 +1909,Cat,Persian,52,Orange,Small,16.055728147661675,1,0,1,82,0,0 +1910,Bird,Parakeet,148,Black,Small,9.643669582049476,0,0,34,426,0,0 +1911,Dog,Poodle,160,White,Large,13.253642026491791,0,0,19,436,1,0 +1912,Cat,Persian,114,Gray,Medium,9.651794564037464,0,0,24,142,1,0 +1913,Dog,Labrador,14,White,Large,8.627749236540737,1,1,4,473,1,1 +1914,Rabbit,Rabbit,178,Brown,Large,5.744644204467654,1,0,36,55,0,0 +1915,Dog,Labrador,156,Black,Large,11.5202468609868,1,0,77,360,0,1 +1916,Dog,Poodle,110,Orange,Medium,8.278628983166051,0,0,58,439,0,0 +1917,Rabbit,Rabbit,53,Brown,Medium,20.819600124563596,1,1,32,286,0,0 +1918,Dog,Poodle,152,Brown,Medium,20.832425493735883,1,0,9,210,0,1 +1919,Dog,Golden Retriever,79,Black,Small,15.553367034113514,1,0,80,440,0,0 +1920,Cat,Siamese,22,Orange,Medium,23.56039129663931,1,0,67,178,1,1 +1921,Rabbit,Rabbit,30,Gray,Small,25.398851851175397,0,0,20,407,0,0 +1922,Rabbit,Rabbit,4,Orange,Small,17.81322726559392,1,0,74,57,0,1 +1923,Cat,Persian,59,White,Medium,18.949119720139578,1,0,62,72,0,1 +1924,Bird,Parakeet,8,Brown,Medium,9.20369138380459,0,1,58,214,0,0 +1925,Dog,Labrador,12,White,Medium,26.951206820460072,1,1,22,387,0,1 +1926,Dog,Poodle,119,Gray,Small,1.701975288253365,0,0,67,265,0,0 +1927,Rabbit,Rabbit,159,White,Large,11.109471613842524,1,1,75,446,0,0 +1928,Bird,Parakeet,34,Orange,Medium,21.932212224945825,1,0,89,72,0,1 +1929,Rabbit,Rabbit,82,White,Medium,3.322595780463676,0,1,48,466,1,0 +1930,Rabbit,Rabbit,98,Gray,Medium,6.311916295420486,1,0,8,270,0,1 +1931,Rabbit,Rabbit,127,Gray,Small,18.089286108022684,1,0,73,498,0,0 +1932,Dog,Golden Retriever,75,Gray,Small,17.23981647244318,0,1,15,353,0,0 +1933,Bird,Parakeet,105,Orange,Large,22.428920587797695,1,1,32,233,0,0 +1934,Rabbit,Rabbit,49,Gray,Medium,24.56576653355698,0,0,53,318,1,0 +1935,Dog,Labrador,79,Brown,Small,4.728807776838405,1,0,67,18,0,1 +1936,Bird,Parakeet,93,Gray,Large,1.5330487034820195,0,0,4,112,0,0 +1937,Dog,Labrador,154,White,Medium,20.999517070108276,0,1,30,62,1,0 +1938,Dog,Poodle,18,White,Small,9.740362077224418,0,0,13,371,0,0 +1939,Rabbit,Rabbit,96,White,Medium,19.153533503620654,0,0,21,183,0,0 +1940,Bird,Parakeet,38,Orange,Medium,5.365793053804516,1,0,41,276,0,1 +1941,Bird,Parakeet,87,White,Small,14.602161353312924,0,0,19,161,0,0 +1942,Cat,Persian,88,Orange,Small,15.62831763583967,1,0,48,242,1,0 +1943,Bird,Parakeet,50,Orange,Small,17.96725667482327,0,0,86,144,0,0 +1944,Dog,Golden Retriever,8,Gray,Medium,15.881930473586367,0,0,63,145,0,1 +1945,Dog,Poodle,169,Brown,Medium,15.375032336632703,1,0,20,80,0,1 +1946,Dog,Golden Retriever,134,Gray,Large,10.649290465531811,1,0,64,321,0,0 +1947,Bird,Parakeet,69,Gray,Medium,3.0642268532245005,1,0,37,434,0,1 +1948,Bird,Parakeet,172,Orange,Small,3.596085707108427,1,0,31,320,0,0 +1949,Cat,Persian,121,Orange,Medium,13.579569957807456,0,0,74,42,0,0 +1950,Rabbit,Rabbit,64,White,Large,11.318000706808798,1,0,18,312,0,0 +1951,Bird,Parakeet,157,White,Large,15.967909335560435,0,0,20,393,1,0 +1952,Dog,Poodle,156,Gray,Small,9.840306908431742,0,0,82,292,0,0 +1953,Bird,Parakeet,139,Black,Small,20.949392652204406,1,0,11,175,0,0 +1954,Bird,Parakeet,121,Black,Large,26.054689838275806,1,0,86,375,0,0 +1955,Cat,Persian,65,White,Large,9.667032474819163,1,0,44,68,1,0 +1956,Cat,Persian,107,Black,Large,6.596536230231829,0,1,82,55,1,0 +1957,Dog,Labrador,147,White,Small,4.369236653368585,1,0,64,118,1,1 +1958,Rabbit,Rabbit,137,Orange,Large,18.871367176553562,1,1,34,203,0,0 +1959,Bird,Parakeet,155,Gray,Large,13.871823743076513,1,0,87,419,1,0 +1960,Cat,Persian,162,Brown,Small,6.452455798692379,0,1,65,414,0,0 +1961,Dog,Poodle,172,Gray,Medium,19.940965265463806,0,0,55,54,0,0 +1962,Bird,Parakeet,100,White,Medium,5.306012307153248,1,0,74,37,0,1 +1963,Dog,Labrador,82,White,Large,17.074720469590623,1,1,1,242,1,0 +1964,Dog,Golden Retriever,143,Gray,Medium,16.287303067132676,1,0,38,200,1,1 +1965,Cat,Persian,51,White,Medium,22.292606234396416,1,0,9,235,1,1 +1966,Rabbit,Rabbit,141,Black,Medium,2.5981795582793357,0,0,71,473,1,0 +1967,Rabbit,Rabbit,83,Black,Medium,26.74831502368858,1,1,17,14,0,0 +1968,Dog,Golden Retriever,177,White,Large,21.386760058257764,1,0,52,143,0,0 +1969,Rabbit,Rabbit,112,White,Medium,7.267653952114104,0,0,18,76,0,0 +1970,Cat,Siamese,56,Black,Medium,6.31875510968245,0,0,63,472,1,0 +1971,Cat,Siamese,179,White,Small,13.51814621016972,1,0,33,477,0,0 +1972,Cat,Siamese,165,White,Large,24.70939455679331,1,0,4,308,1,0 +1973,Rabbit,Rabbit,145,Gray,Small,25.445549801034108,1,0,87,439,0,0 +1974,Cat,Siamese,157,Brown,Small,16.093226672042107,0,1,75,262,0,0 +1975,Rabbit,Rabbit,161,Black,Large,25.963515017915388,1,0,52,184,0,0 +1976,Rabbit,Rabbit,60,Gray,Medium,1.1578675665590266,0,0,9,54,0,0 +1977,Bird,Parakeet,120,Brown,Small,2.0200643144163015,1,0,64,307,1,0 +1978,Cat,Siamese,138,White,Large,27.44282621546554,1,0,30,3,0,0 +1979,Dog,Poodle,175,White,Medium,23.484625966691564,0,0,8,298,0,0 +1980,Rabbit,Rabbit,23,Gray,Large,14.070475689261185,0,0,5,39,0,0 +1981,Rabbit,Rabbit,149,White,Small,22.854693812861626,1,0,76,229,0,0 +1982,Rabbit,Rabbit,13,Orange,Medium,28.713544389979226,1,0,63,376,0,1 +1983,Rabbit,Rabbit,101,Gray,Medium,23.390596192557265,0,0,1,285,0,0 +1984,Cat,Persian,161,Brown,Large,25.428768680731338,1,0,15,137,1,0 +1985,Bird,Parakeet,24,Gray,Medium,23.05759086031179,1,0,89,477,0,1 +1986,Rabbit,Rabbit,108,Black,Small,5.878121680572706,0,0,82,102,0,0 +1987,Dog,Poodle,11,Gray,Small,25.173649179726365,1,0,38,6,0,1 +1988,Dog,Poodle,17,White,Small,6.658155780702091,1,0,42,163,0,1 +1989,Cat,Siamese,175,White,Large,26.885728513280263,1,1,46,418,0,0 +1990,Cat,Persian,25,Orange,Medium,14.300847044351201,1,0,53,471,0,1 +1991,Rabbit,Rabbit,94,Orange,Medium,20.632325817019826,0,0,26,422,0,0 +1992,Bird,Parakeet,21,Orange,Medium,15.41998458853866,1,0,61,419,0,1 +1993,Cat,Persian,84,Brown,Small,3.725736953081459,1,1,67,364,0,0 +1994,Cat,Persian,140,Orange,Medium,9.430946920999288,1,0,54,275,1,1 +1995,Bird,Parakeet,37,Brown,Small,29.528223765913175,1,1,22,48,1,0 +1996,Dog,Golden Retriever,42,Orange,Medium,28.860727798424705,0,0,26,95,0,0 +1997,Bird,Parakeet,145,Brown,Small,10.294120032010872,1,1,83,58,0,0 +1998,Rabbit,Rabbit,101,Gray,Medium,29.892607323111548,1,0,60,230,0,1 +1999,Dog,Golden Retriever,48,Gray,Large,25.52098466941009,0,0,39,176,0,0 +2000,Dog,Golden Retriever,103,White,Large,18.717825483535385,1,0,36,89,1,0 +2001,Dog,Poodle,28,Brown,Small,2.163613987569275,1,1,21,281,1,0 +2002,Bird,Parakeet,41,Black,Small,27.141958056613007,1,0,41,35,0,0 +2003,Bird,Parakeet,99,White,Medium,17.165449098665274,0,0,48,170,0,0 +2004,Rabbit,Rabbit,47,Gray,Medium,26.185808384880318,0,0,26,125,0,0 +2005,Bird,Parakeet,146,Brown,Large,25.198444477266527,1,0,63,482,0,0 +2006,Bird,Parakeet,105,Orange,Medium,25.157180240831497,1,0,77,298,1,1 +2007,Cat,Siamese,124,Black,Small,21.247041960732247,1,0,36,354,0,0 +2008,Cat,Persian,13,Gray,Small,9.45103902160541,1,0,24,203,0,1 +2009,Cat,Persian,141,White,Small,10.748647474591076,0,1,21,288,0,0 +2010,Dog,Labrador,100,Brown,Small,9.945607164897904,1,0,18,355,0,1 +2011,Bird,Parakeet,31,Black,Small,1.5813339864443763,1,0,15,461,0,0 +2012,Dog,Labrador,130,Brown,Large,17.894498367229524,1,0,79,96,0,1 +2013,Bird,Parakeet,16,Orange,Large,21.266176442934952,0,1,6,237,0,0 +2014,Bird,Parakeet,137,White,Small,25.762113812230904,0,0,43,172,1,0 +2015,Bird,Parakeet,73,Brown,Medium,5.064797568099181,1,0,31,300,1,1 +2016,Dog,Labrador,166,Gray,Small,16.78131132032725,0,1,28,87,0,0 +2017,Bird,Parakeet,54,White,Large,1.232236079539703,0,0,8,398,1,0 +2018,Rabbit,Rabbit,100,Gray,Small,3.7577157836111548,0,0,29,350,0,0 +2019,Rabbit,Rabbit,109,Brown,Large,2.572140581973799,1,0,21,16,1,0 +2020,Cat,Siamese,87,White,Large,22.03886298644912,1,1,81,139,1,0 +2021,Cat,Persian,173,Gray,Small,3.3507273524504915,1,0,88,253,0,0 +2022,Cat,Siamese,162,Orange,Small,21.77713646520491,0,0,58,277,1,0 +2023,Bird,Parakeet,137,Orange,Medium,8.239116427906918,1,0,60,150,0,1 +2024,Bird,Parakeet,8,Brown,Large,26.453675860211245,0,0,50,345,0,0 +2025,Dog,Poodle,7,White,Medium,24.58134966255061,1,1,31,333,0,1 +2026,Rabbit,Rabbit,36,Black,Small,28.77707630678302,1,0,47,139,1,0 +2027,Rabbit,Rabbit,18,White,Large,26.629098448282924,1,0,34,113,1,1 +2028,Rabbit,Rabbit,3,Orange,Medium,28.06655315437407,0,1,2,67,0,0 +2029,Dog,Labrador,28,Gray,Medium,25.903529498885923,0,0,67,365,0,1 +2030,Bird,Parakeet,70,Black,Medium,21.108075117830808,1,0,72,178,0,1 +2031,Rabbit,Rabbit,172,Brown,Small,5.9375154116376425,1,1,52,124,1,0 +2032,Dog,Golden Retriever,149,Black,Small,8.879518046768695,0,0,7,396,0,0 +2033,Rabbit,Rabbit,28,Brown,Small,7.169082017777919,1,0,76,312,1,0 +2034,Cat,Siamese,54,Black,Large,18.503987842087962,1,0,19,310,0,0 +2035,Dog,Labrador,65,Brown,Small,15.60497820123444,1,0,15,337,0,1 +2036,Bird,Parakeet,100,Brown,Large,26.145319917594286,1,1,78,362,0,0 +2037,Bird,Parakeet,21,Black,Medium,28.627513128473193,0,0,48,411,1,1 +2038,Dog,Poodle,13,Black,Medium,12.740936798926185,0,0,14,357,0,1 +2039,Rabbit,Rabbit,57,Brown,Small,15.684166045143558,0,0,3,232,1,0 +2040,Cat,Siamese,49,Orange,Medium,29.173974347320037,1,0,50,427,0,1 +2041,Cat,Siamese,118,Black,Small,9.010929307071162,0,1,54,146,0,0 +2042,Bird,Parakeet,54,White,Large,23.979332485405596,1,0,6,429,1,0 +2043,Rabbit,Rabbit,138,Gray,Medium,7.531957167722142,0,1,49,110,1,0 +2044,Dog,Labrador,53,Brown,Small,20.762343347332887,1,0,23,362,1,1 +2045,Rabbit,Rabbit,44,Brown,Medium,8.35485320151836,1,0,78,357,0,1 +2046,Rabbit,Rabbit,47,Gray,Medium,26.62826827611022,0,0,78,237,0,0 +2047,Cat,Siamese,53,Orange,Small,17.28286036836363,0,1,20,2,0,0 +2048,Rabbit,Rabbit,74,Gray,Medium,6.392075585357153,1,0,77,210,0,1 +2049,Rabbit,Rabbit,46,Black,Medium,7.506706095256095,0,0,60,403,1,0 +2050,Bird,Parakeet,13,Gray,Medium,15.321626675534908,0,0,45,24,0,1 +2051,Rabbit,Rabbit,165,Brown,Medium,28.886458741671486,0,0,61,330,0,0 +2052,Rabbit,Rabbit,102,Gray,Large,23.31962275262598,1,0,56,183,0,0 +2053,Cat,Siamese,24,White,Medium,15.160979641949508,1,1,2,464,0,0 +2054,Dog,Labrador,2,Black,Medium,10.793855192304296,1,0,45,60,0,1 +2055,Dog,Golden Retriever,81,Gray,Small,16.928786307104836,0,0,40,122,0,0 +2056,Bird,Parakeet,100,Brown,Large,22.70246416942997,1,0,24,496,0,0 +2057,Cat,Persian,142,Black,Small,23.7339290651312,1,0,29,54,0,0 +2058,Rabbit,Rabbit,138,Brown,Small,10.413655144811395,1,0,67,162,1,0 +2059,Bird,Parakeet,163,Orange,Small,21.91361877820244,0,0,68,461,0,0 +2060,Bird,Parakeet,153,White,Large,20.075874621049397,1,0,66,186,0,0 +2061,Dog,Labrador,131,Orange,Small,28.814256467892523,1,0,84,38,1,1 +2062,Rabbit,Rabbit,111,White,Small,28.00161842473243,1,0,54,184,1,0 +2063,Rabbit,Rabbit,102,Gray,Small,23.892000442503733,0,0,53,82,1,0 +2064,Cat,Siamese,17,Black,Medium,21.978445976792923,0,0,79,194,1,1 +2065,Rabbit,Rabbit,12,Gray,Small,8.947206101424,0,0,49,33,0,0 +2066,Cat,Siamese,116,White,Large,25.53299242685747,0,0,32,112,0,0 +2067,Dog,Poodle,24,White,Large,13.397665407687619,1,0,61,424,0,0 +2068,Cat,Siamese,29,White,Medium,8.894975732675295,1,0,42,405,0,1 +2069,Dog,Poodle,171,Brown,Large,12.140661980650412,1,0,1,229,1,0 +2070,Rabbit,Rabbit,153,Black,Large,25.24212921047873,0,0,63,282,0,0 +2071,Rabbit,Rabbit,122,Gray,Medium,5.310363251758665,0,0,35,404,1,0 +2072,Rabbit,Rabbit,90,Gray,Small,14.228603492474852,0,0,51,424,1,0 +2073,Dog,Poodle,65,Orange,Medium,15.278370241355201,0,1,71,286,0,0 +2074,Rabbit,Rabbit,84,White,Small,2.0622612303657624,1,0,45,154,1,0 +2075,Dog,Golden Retriever,119,White,Small,6.676634856896794,0,0,77,164,0,0 +2076,Cat,Persian,156,White,Large,24.975530428699837,0,1,65,449,0,0 +2077,Dog,Labrador,16,Gray,Medium,25.09225426924806,1,0,14,181,1,1 +2078,Cat,Persian,175,Orange,Large,9.480037432643192,1,0,3,238,0,0 +2079,Rabbit,Rabbit,104,Brown,Large,26.37201980333986,1,0,24,136,1,0 +2080,Bird,Parakeet,92,Black,Small,23.381705232982085,1,0,65,338,0,0 +2081,Rabbit,Rabbit,27,Brown,Large,13.119317030066874,0,0,48,425,0,0 +2082,Dog,Labrador,170,Gray,Medium,22.703783987746096,1,0,10,419,1,1 +2083,Rabbit,Rabbit,175,White,Large,28.878704304649382,1,0,13,265,0,0 +2084,Dog,Poodle,162,Black,Large,14.310121823895116,1,0,73,186,0,0 +2085,Cat,Persian,165,Gray,Small,7.914388189651025,1,0,14,39,0,0 +2086,Cat,Persian,134,Gray,Medium,28.31267850145452,0,0,10,309,1,0 +2087,Bird,Parakeet,135,Gray,Large,18.508584368334436,1,0,40,492,0,0 +2088,Bird,Parakeet,153,Black,Small,4.052689711932333,0,0,23,166,1,0 +2089,Cat,Persian,33,Black,Large,17.607801659843993,1,0,55,390,0,0 +2090,Bird,Parakeet,55,Orange,Medium,15.516845794000663,1,0,63,259,0,1 +2091,Dog,Labrador,7,Orange,Large,25.81207792819134,1,0,71,142,1,1 +2092,Dog,Poodle,57,Brown,Large,16.39016050314212,1,0,42,203,0,0 +2093,Dog,Golden Retriever,126,Black,Small,8.401740595984219,1,1,66,175,0,0 +2094,Cat,Siamese,12,Black,Large,20.59367785208177,0,1,79,261,1,0 +2095,Rabbit,Rabbit,16,Black,Small,16.734688123531825,1,0,73,251,1,1 +2096,Dog,Labrador,43,Orange,Medium,7.758294219739301,1,0,47,328,0,1 +2097,Rabbit,Rabbit,117,Gray,Large,17.63520788034433,0,0,81,418,0,0 +2098,Dog,Poodle,135,White,Medium,24.824531758800898,1,0,52,199,1,1 +2099,Rabbit,Rabbit,111,Orange,Medium,1.8285566139963025,1,1,18,198,1,0 +2100,Bird,Parakeet,51,Black,Large,19.29468012070824,1,0,79,195,1,0 +2101,Rabbit,Rabbit,160,Orange,Small,11.13797067774737,1,1,18,313,0,0 +2102,Cat,Persian,75,Brown,Medium,9.4874719270233,1,0,24,76,1,1 +2103,Rabbit,Rabbit,8,Black,Medium,12.385044760472226,0,0,23,18,0,1 +2104,Rabbit,Rabbit,45,Brown,Large,10.918657263191468,0,0,12,61,0,0 +2105,Dog,Poodle,59,Black,Large,25.134934094544807,1,0,23,326,0,0 +2106,Rabbit,Rabbit,132,Orange,Large,8.969136512269117,1,0,21,393,0,0 +2107,Rabbit,Rabbit,104,Gray,Large,22.261437670090977,1,0,64,280,0,0 +2108,Cat,Persian,157,White,Large,3.727734786629073,0,0,72,427,0,0 +2109,Dog,Poodle,157,Brown,Large,28.590494802048052,0,0,34,249,0,0 +2110,Dog,Labrador,98,Orange,Small,19.047182480208,0,0,2,226,0,0 +2111,Dog,Labrador,156,Gray,Medium,21.494838734292244,1,0,69,126,0,1 +2112,Cat,Siamese,79,Orange,Small,9.64314063073908,1,1,31,57,0,0 +2113,Dog,Poodle,124,Brown,Small,12.950800492535324,1,0,68,408,0,0 +2114,Cat,Siamese,172,White,Large,7.4895732018583105,0,0,70,476,0,0 +2115,Cat,Persian,4,Orange,Medium,28.043546488264493,0,1,87,375,0,0 +2116,Bird,Parakeet,90,Black,Small,15.155028965939954,1,0,19,203,0,0 +2117,Cat,Persian,104,Gray,Large,19.28299535408171,0,0,72,242,1,0 +2118,Cat,Persian,137,Orange,Large,22.293888885390746,1,1,13,366,1,0 +2119,Bird,Parakeet,119,Gray,Medium,11.76207664188663,1,0,65,423,1,1 +2120,Cat,Persian,133,Black,Small,21.882947981635095,1,0,6,331,0,0 +2121,Bird,Parakeet,56,White,Small,20.603782202933864,1,0,71,499,0,0 +2122,Rabbit,Rabbit,96,White,Small,21.46412068234445,1,0,27,325,0,0 +2123,Dog,Golden Retriever,178,Black,Large,15.187667105933773,0,0,79,251,1,0 +2124,Dog,Golden Retriever,75,Gray,Medium,12.105184086456896,1,1,52,393,0,0 +2125,Bird,Parakeet,143,Orange,Medium,7.440383458060706,1,0,87,321,0,1 +2126,Cat,Siamese,32,Black,Small,24.914164231225893,0,0,48,375,0,0 +2127,Cat,Siamese,28,Brown,Large,18.642151349418683,1,0,88,492,0,0 +2128,Bird,Parakeet,22,Gray,Small,13.886213686827354,0,0,79,408,1,0 +2129,Bird,Parakeet,17,White,Medium,28.37588129460773,0,1,68,198,1,0 +2130,Rabbit,Rabbit,59,Black,Large,2.3873255627041257,0,0,44,487,1,0 +2131,Dog,Poodle,90,White,Small,25.65498641094638,1,1,17,342,0,0 +2132,Rabbit,Rabbit,106,Gray,Small,29.9298951954332,1,0,38,239,1,0 +2133,Rabbit,Rabbit,26,White,Large,26.721321215603155,1,0,10,138,0,0 +2134,Rabbit,Rabbit,25,Black,Medium,24.502660982853133,0,1,54,121,1,0 +2135,Cat,Persian,138,Gray,Large,11.854873808783415,1,1,6,419,0,0 +2136,Cat,Persian,45,Brown,Small,7.7081112856065115,1,1,6,48,0,0 +2137,Cat,Siamese,83,Black,Small,22.31066509448843,1,0,52,303,0,0 +2138,Bird,Parakeet,11,Orange,Small,9.832685622368484,0,0,15,114,0,0 +2139,Bird,Parakeet,104,Black,Small,6.1051507824941496,0,0,68,342,1,0 +2140,Bird,Parakeet,68,Gray,Medium,23.809250726004148,0,1,57,250,1,0 +2141,Bird,Parakeet,95,Black,Medium,10.063765914864945,1,0,56,490,0,1 +2142,Rabbit,Rabbit,156,Brown,Small,27.51881015513281,1,0,7,6,0,0 +2143,Rabbit,Rabbit,65,Brown,Medium,25.378346699754633,1,0,51,218,0,1 +2144,Rabbit,Rabbit,49,Orange,Medium,25.5815695557684,1,0,58,76,1,1 +2145,Cat,Persian,175,White,Medium,23.37552205561896,1,1,64,178,0,0 +2146,Cat,Persian,52,Gray,Small,26.545561289467365,1,0,26,144,0,0 +2147,Bird,Parakeet,102,Gray,Large,24.366659873165485,1,0,58,415,0,0 +2148,Bird,Parakeet,57,White,Large,18.47282954366713,0,0,31,153,0,0 +2149,Bird,Parakeet,47,Brown,Large,9.578858838874032,0,0,66,120,1,0 +2150,Dog,Poodle,159,Brown,Small,26.758066603210324,1,0,13,123,0,0 +2151,Cat,Siamese,174,Black,Medium,25.377505060422564,1,0,18,149,0,1 +2152,Cat,Persian,26,Orange,Medium,14.713500344105807,0,0,75,396,1,0 +2153,Rabbit,Rabbit,114,Orange,Small,2.1332818940567004,1,0,13,380,0,0 +2154,Bird,Parakeet,5,Orange,Small,19.51776809465549,1,0,24,267,0,1 +2155,Rabbit,Rabbit,84,Black,Large,2.46730875662087,1,0,57,326,0,0 +2156,Dog,Labrador,52,Orange,Large,27.86211889360381,1,0,29,0,0,1 +2157,Dog,Golden Retriever,105,White,Small,28.887306442133905,1,0,56,160,0,0 +2158,Cat,Siamese,54,Black,Large,6.495214231088522,1,0,54,243,1,0 +2159,Bird,Parakeet,161,White,Medium,20.615291963592387,0,0,5,374,1,0 +2160,Cat,Persian,178,Black,Small,3.67580113232012,1,1,35,397,1,0 +2161,Dog,Labrador,166,Orange,Small,22.15599092862054,1,0,19,250,1,1 +2162,Bird,Parakeet,155,White,Medium,11.071342318427563,1,0,55,366,0,1 +2163,Dog,Golden Retriever,15,White,Medium,20.750453240182914,1,1,77,63,0,1 +2164,Rabbit,Rabbit,61,White,Medium,11.813418766863158,0,0,69,298,0,0 +2165,Bird,Parakeet,122,Black,Large,5.221457785163836,1,1,44,247,1,0 +2166,Rabbit,Rabbit,21,Gray,Small,12.777189434574177,0,0,78,441,0,0 +2167,Cat,Persian,46,Orange,Large,4.267532272408911,1,1,38,296,0,0 +2168,Cat,Siamese,134,White,Medium,17.72961497433143,1,1,62,464,0,0 +2169,Cat,Siamese,38,Gray,Large,2.1875944323349135,1,0,22,451,0,0 +2170,Rabbit,Rabbit,62,Orange,Medium,16.292270708395932,1,0,73,499,0,1 +2171,Rabbit,Rabbit,86,Orange,Large,26.704690765770685,1,0,40,114,1,0 +2172,Dog,Golden Retriever,78,White,Large,25.29832483346152,0,0,26,294,0,0 +2173,Bird,Parakeet,46,White,Medium,26.60066928916841,1,0,5,0,1,1 +2174,Rabbit,Rabbit,141,Black,Large,27.739415261686112,0,0,54,40,0,0 +2175,Cat,Siamese,3,Gray,Small,4.031308748395133,1,0,86,172,1,1 +2176,Bird,Parakeet,6,Gray,Large,4.733668883395679,0,0,49,87,1,0 +2177,Dog,Labrador,93,White,Large,17.15319738308073,1,1,3,481,1,0 +2178,Cat,Siamese,8,Orange,Small,26.925207384308447,1,0,38,97,0,1 +2179,Bird,Parakeet,172,White,Medium,29.25613423919689,1,0,14,87,0,1 +2180,Dog,Golden Retriever,108,Orange,Small,2.3415956028599987,1,0,47,250,0,0 +2181,Rabbit,Rabbit,161,Orange,Large,11.317448441819144,1,0,14,334,0,0 +2182,Dog,Poodle,75,White,Medium,7.17393338513981,1,0,75,464,0,1 +2183,Dog,Golden Retriever,77,Brown,Large,22.923009619006194,1,1,73,140,1,0 +2184,Cat,Siamese,50,Gray,Large,19.565622187573926,1,0,38,70,0,0 +2185,Rabbit,Rabbit,11,Orange,Small,4.4767529130315165,1,0,64,136,0,1 +2186,Rabbit,Rabbit,95,Orange,Small,15.022139953584173,1,1,44,491,0,0 +2187,Dog,Poodle,156,Brown,Medium,20.87777514876745,1,1,70,446,1,0 +2188,Rabbit,Rabbit,70,Black,Medium,23.59115642379709,1,0,65,98,1,1 +2189,Rabbit,Rabbit,169,Brown,Medium,1.5815285837362474,1,0,25,150,0,1 +2190,Rabbit,Rabbit,16,Brown,Large,26.823617645532806,1,0,16,437,0,1 +2191,Cat,Siamese,68,Orange,Small,17.509160105565208,0,0,15,174,1,0 +2192,Bird,Parakeet,99,Gray,Small,24.545364333837234,1,1,81,166,0,0 +2193,Rabbit,Rabbit,48,Brown,Small,24.43020287041422,1,0,3,342,0,0 +2194,Dog,Poodle,64,Black,Medium,6.505727147858105,1,0,67,409,1,1 +2195,Dog,Poodle,90,White,Small,4.1949914873553595,1,1,37,256,1,0 +2196,Rabbit,Rabbit,76,Brown,Small,21.917330800240972,0,0,81,397,0,0 +2197,Rabbit,Rabbit,139,Brown,Small,5.536380645826953,0,1,55,223,0,0 +2198,Cat,Siamese,101,Black,Medium,25.64818725073699,1,0,38,498,0,1 +2199,Cat,Persian,89,Gray,Small,22.05949503200105,1,0,63,367,0,0 +2200,Cat,Siamese,18,Brown,Medium,25.412935566592616,1,0,1,329,0,1 +2201,Cat,Siamese,19,Brown,Small,9.671367708671037,1,0,71,64,0,1 +2202,Bird,Parakeet,55,Gray,Small,10.181343751906502,1,0,19,68,0,0 +2203,Cat,Persian,102,Black,Small,20.465425134043212,1,0,52,66,0,0 +2204,Dog,Golden Retriever,118,Brown,Medium,1.9119326967094787,1,0,30,432,0,1 +2205,Dog,Poodle,154,Gray,Medium,22.06840564378526,1,1,55,49,0,0 +2206,Rabbit,Rabbit,134,Brown,Small,25.237709054783437,1,0,14,313,0,0 +2207,Dog,Golden Retriever,4,White,Large,13.039744011519016,0,0,13,308,0,0 +2208,Dog,Golden Retriever,108,Orange,Medium,23.18148607256559,1,0,84,90,0,1 +2209,Cat,Persian,115,White,Medium,6.063716592485596,1,1,27,114,0,0 +2210,Rabbit,Rabbit,29,Brown,Small,10.345551420765055,1,0,89,373,1,0 +2211,Rabbit,Rabbit,59,Black,Large,29.254843930203844,1,0,9,56,0,0 +2212,Cat,Siamese,125,Orange,Small,29.603517033243662,1,0,41,341,1,0 +2213,Cat,Persian,98,White,Medium,15.865546537626052,0,0,48,17,0,0 +2214,Cat,Persian,17,Gray,Medium,28.09382395245243,1,0,12,271,0,1 +2215,Cat,Siamese,107,Black,Small,16.15918966364878,0,0,10,129,0,0 +2216,Dog,Labrador,43,White,Small,9.456055716673562,1,0,6,494,0,1 +2217,Cat,Siamese,80,Gray,Large,8.740079354864383,1,1,34,268,1,0 +2218,Dog,Poodle,162,Brown,Large,27.955665455676314,1,1,52,428,1,0 +2219,Cat,Persian,8,Gray,Medium,6.000945280241914,0,0,41,79,1,1 +2220,Dog,Golden Retriever,141,White,Large,17.809341287852728,0,0,83,455,0,0 +2221,Cat,Persian,106,White,Large,17.110226973818868,0,0,6,298,0,0 +2222,Bird,Parakeet,8,Brown,Medium,22.449028854936852,0,0,2,455,0,1 +2223,Dog,Poodle,96,Gray,Medium,15.478044318218467,1,1,15,309,0,0 +2224,Bird,Parakeet,94,Gray,Medium,8.896284160535892,1,0,17,201,0,1 +2225,Cat,Siamese,65,Brown,Medium,5.9124984893157775,1,0,16,224,1,1 +2226,Bird,Parakeet,171,Gray,Small,19.00374530167086,0,1,28,158,1,0 +2227,Dog,Labrador,39,Brown,Medium,21.85073194874376,1,0,85,203,0,1 +2228,Rabbit,Rabbit,81,Black,Medium,17.516117081778948,1,0,72,136,0,1 +2229,Cat,Siamese,84,White,Medium,22.41218703651465,0,0,10,484,0,0 +2230,Dog,Labrador,88,Black,Large,5.104114871048844,1,0,10,72,0,1 +2231,Bird,Parakeet,86,Orange,Medium,26.59163565335093,0,0,10,47,0,0 +2232,Dog,Labrador,93,Orange,Small,17.88946974384659,0,0,73,17,1,0 +2233,Bird,Parakeet,86,Orange,Small,4.678473879885318,1,0,28,477,0,0 +2234,Bird,Parakeet,44,Brown,Medium,14.395830921966315,1,0,24,479,0,1 +2235,Bird,Parakeet,137,Gray,Small,25.498960398111432,1,0,19,487,0,0 +2236,Bird,Parakeet,165,Black,Large,27.029303610141643,1,0,68,470,0,0 +2237,Cat,Siamese,74,Black,Medium,8.674835644964084,1,0,56,266,1,1 +2238,Cat,Persian,36,Black,Small,21.62670891660314,1,0,67,445,0,0 +2239,Rabbit,Rabbit,101,Black,Small,3.899572177029524,1,0,42,339,1,0 +2240,Cat,Siamese,156,Orange,Medium,1.9728302709525765,1,0,12,18,0,1 +2241,Cat,Persian,170,Black,Small,17.25156966077834,0,0,18,99,1,0 +2242,Cat,Siamese,171,Gray,Medium,21.14999980051565,0,0,23,115,0,0 +2243,Dog,Labrador,170,Orange,Small,24.077815204118558,1,0,84,345,0,1 +2244,Rabbit,Rabbit,104,Black,Large,10.866270915204705,1,0,6,73,1,0 +2245,Rabbit,Rabbit,66,Brown,Small,1.1892369024377227,0,0,53,279,1,0 +2246,Bird,Parakeet,55,White,Small,14.90605787622926,1,0,42,268,1,0 +2247,Rabbit,Rabbit,176,White,Medium,1.4597528158334425,0,0,66,124,0,0 +2248,Dog,Labrador,91,White,Small,3.667550809453198,1,0,30,115,1,1 +2249,Rabbit,Rabbit,22,Orange,Medium,10.473981653420703,0,1,9,173,1,0 +2250,Cat,Persian,101,Black,Large,13.677989437245138,1,0,42,180,0,0 +2251,Rabbit,Rabbit,155,Orange,Medium,9.104613795659809,1,1,34,225,1,0 +2252,Dog,Golden Retriever,121,Gray,Medium,17.874092728636526,0,0,40,156,0,0 +2253,Dog,Golden Retriever,93,Brown,Small,2.6051438873247603,1,0,88,163,0,0 +2254,Dog,Labrador,108,Brown,Large,5.192811596947957,1,0,17,70,0,1 +2255,Dog,Labrador,141,White,Medium,21.162493621907572,0,0,29,498,1,1 +2256,Rabbit,Rabbit,173,White,Small,5.122084754339614,0,0,50,221,0,0 +2257,Rabbit,Rabbit,10,White,Small,19.55348084554225,1,0,76,125,0,1 +2258,Rabbit,Rabbit,9,Black,Medium,2.585854001847601,1,1,49,144,0,1 +2259,Rabbit,Rabbit,155,Gray,Small,12.814086094455071,1,1,73,175,0,0 +2260,Bird,Parakeet,34,Gray,Medium,6.144874633514288,0,0,61,460,0,0 +2261,Bird,Parakeet,129,Gray,Medium,4.179452193520406,1,1,83,151,0,0 +2262,Cat,Persian,160,Gray,Medium,17.4439507215356,0,1,46,181,1,0 +2263,Bird,Parakeet,89,White,Large,27.92173738816303,1,1,46,374,1,0 +2264,Dog,Poodle,93,Brown,Medium,2.588824389782916,1,0,17,478,1,1 +2265,Cat,Persian,71,White,Small,13.225073198322992,1,0,66,346,1,0 +2266,Dog,Golden Retriever,67,Gray,Large,21.518527694617866,1,0,5,497,0,0 +2267,Dog,Poodle,148,White,Small,14.417947118946984,1,0,54,84,0,0 +2268,Dog,Labrador,117,Gray,Large,24.388382437052112,1,1,16,363,0,0 +2269,Bird,Parakeet,94,Orange,Large,6.209714160059819,1,0,81,382,0,0 +2270,Bird,Parakeet,163,Brown,Large,28.828231945804475,1,0,7,120,1,0 +2271,Cat,Siamese,99,Orange,Large,12.169716205403676,1,1,8,128,0,0 +2272,Dog,Golden Retriever,113,Black,Small,10.905519355383978,1,0,72,462,1,0 +2273,Dog,Labrador,103,White,Medium,26.36356804059181,1,1,87,450,0,1 +2274,Dog,Golden Retriever,91,Black,Small,17.35214392486123,1,0,82,5,1,0 +2275,Cat,Persian,90,White,Medium,15.976885915181825,1,0,38,333,0,1 +2276,Cat,Persian,17,Orange,Small,22.082359666324496,1,0,54,182,0,1 +2277,Dog,Poodle,18,Gray,Large,17.81519291015162,1,0,76,37,1,1 +2278,Rabbit,Rabbit,173,Gray,Large,5.712953697956345,1,0,15,416,0,0 +2279,Bird,Parakeet,116,White,Medium,20.55173106308296,1,1,50,115,1,0 +2280,Bird,Parakeet,93,Gray,Medium,4.870597499181725,1,1,56,101,0,0 +2281,Cat,Persian,91,Orange,Small,24.3698734546878,1,0,9,7,0,0 +2282,Dog,Poodle,82,Gray,Medium,29.70236133365586,1,0,8,217,1,1 +2283,Dog,Labrador,68,Orange,Small,11.944241124268512,1,1,19,206,0,0 +2284,Bird,Parakeet,114,Black,Medium,13.897921047858365,1,0,51,77,1,1 +2285,Bird,Parakeet,1,Orange,Medium,2.346576009001557,1,0,72,367,0,1 +2286,Rabbit,Rabbit,58,Brown,Medium,1.3982624543947701,1,1,57,403,0,0 +2287,Bird,Parakeet,154,Brown,Large,27.721160711413503,0,1,68,54,0,0 +2288,Bird,Parakeet,109,Brown,Large,4.73009775273657,0,0,31,303,0,0 +2289,Cat,Siamese,148,White,Small,13.92758367922464,0,0,3,466,0,0 +2290,Rabbit,Rabbit,116,White,Medium,24.64528409181128,1,0,83,439,0,1 +2291,Cat,Persian,87,White,Small,26.790244037921266,1,0,43,480,0,0 +2292,Bird,Parakeet,10,Orange,Medium,16.06253287322056,0,1,29,227,0,0 +2293,Rabbit,Rabbit,59,White,Large,17.385943224302544,1,1,10,87,0,0 +2294,Cat,Siamese,41,Black,Large,20.4208352014349,1,0,12,9,0,0 +2295,Rabbit,Rabbit,105,Brown,Medium,28.88009163416774,1,0,46,307,0,1 +2296,Rabbit,Rabbit,133,Orange,Medium,18.477428867390646,1,1,30,433,0,0 +2297,Cat,Persian,122,Brown,Large,17.22478252423404,1,0,82,289,0,0 +2298,Rabbit,Rabbit,145,White,Large,1.4214899227137296,0,0,88,163,0,0 +2299,Bird,Parakeet,22,Orange,Small,3.3475413586483262,1,1,32,238,0,0 +2300,Rabbit,Rabbit,98,Gray,Large,17.737977172095892,0,0,73,316,1,0 +2301,Cat,Siamese,169,Black,Medium,28.150971246408893,1,0,47,483,1,1 +2302,Dog,Golden Retriever,68,White,Small,26.134049103582456,1,0,88,143,0,0 +2303,Rabbit,Rabbit,139,White,Medium,29.494788766773933,0,0,63,334,0,0 +2304,Dog,Poodle,56,Black,Small,16.307187089276944,0,0,26,17,1,0 +2305,Dog,Golden Retriever,179,Black,Medium,23.84999721469524,1,0,3,70,0,1 +2306,Cat,Siamese,149,Gray,Large,25.371609613190873,1,1,23,174,1,0 +2307,Cat,Siamese,99,White,Medium,19.792918062594993,1,0,75,341,1,1 +2308,Bird,Parakeet,76,White,Small,10.82374722222071,0,0,74,287,0,0 +2309,Bird,Parakeet,26,Brown,Medium,14.629374605076038,0,0,50,298,0,0 +2310,Cat,Siamese,115,Orange,Small,25.879595095063465,1,0,36,338,1,0 +2311,Cat,Persian,147,Orange,Medium,9.657889853482754,0,0,13,423,1,0 +2312,Cat,Persian,109,Brown,Large,27.788550806732808,1,0,29,247,0,0 +2313,Dog,Poodle,47,Orange,Large,7.664383901257287,0,0,38,192,0,0 +2314,Rabbit,Rabbit,71,Black,Medium,22.73597603954664,1,0,77,418,0,1 +2315,Bird,Parakeet,154,Orange,Medium,19.286217796026413,0,1,84,324,0,0 +2316,Rabbit,Rabbit,40,Orange,Medium,22.738383792502106,0,0,5,305,1,0 +2317,Dog,Golden Retriever,133,Brown,Small,13.68307960054525,1,0,20,267,1,0 +2318,Rabbit,Rabbit,149,Gray,Medium,28.046590814065553,1,0,78,203,0,1 +2319,Dog,Poodle,132,Orange,Medium,19.341693869932843,0,0,14,340,1,0 +2320,Rabbit,Rabbit,23,Orange,Small,9.360578332073432,0,0,8,122,0,0 +2321,Dog,Labrador,106,Gray,Large,19.515197048912167,0,0,48,57,0,0 +2322,Bird,Parakeet,84,Brown,Medium,9.317656669003904,1,0,13,338,0,1 +2323,Bird,Parakeet,143,Gray,Medium,9.515339361036352,1,1,24,446,0,0 +2324,Rabbit,Rabbit,116,Brown,Large,12.588356837005168,0,0,79,200,0,0 +2325,Cat,Siamese,109,Black,Large,25.72048210950445,1,0,65,490,0,0 +2326,Rabbit,Rabbit,136,Brown,Small,15.508638357425767,1,0,86,432,1,0 +2327,Dog,Labrador,41,Black,Medium,20.688124424676015,1,0,34,111,1,1 +2328,Cat,Persian,112,Black,Medium,12.367064467959427,1,1,51,127,0,1 +2329,Rabbit,Rabbit,147,Brown,Small,8.644557754399562,1,0,3,35,0,1 +2330,Cat,Persian,112,White,Large,28.68456466077071,0,0,78,233,0,1 +2331,Rabbit,Rabbit,103,Brown,Small,18.503856166568816,0,0,58,309,1,0 +2332,Cat,Siamese,46,Orange,Large,28.08458881889883,1,0,72,46,0,0 +2333,Dog,Labrador,156,Orange,Small,6.898977063751136,1,0,55,114,0,1 +2334,Dog,Labrador,68,White,Large,5.25993075347505,1,1,80,415,0,0 +2335,Rabbit,Rabbit,122,Black,Small,25.818864306593206,1,0,42,254,0,1 +2336,Dog,Labrador,15,White,Large,20.746599165900292,1,0,21,482,0,1 +2337,Dog,Labrador,165,Orange,Large,6.410776478355599,1,0,10,222,1,0 +2338,Bird,Parakeet,129,White,Large,8.811431319242594,0,0,57,123,1,0 +2339,Dog,Golden Retriever,111,Brown,Large,24.219254304234024,1,1,25,436,0,1 +2340,Rabbit,Rabbit,24,White,Large,14.162189979882147,0,0,56,159,0,0 +2341,Cat,Siamese,27,Black,Medium,20.445674688676636,0,0,38,445,0,1 +2342,Cat,Persian,40,Brown,Large,10.232208641944316,1,0,86,420,1,1 +2343,Rabbit,Rabbit,164,Gray,Medium,12.055668630471425,0,0,61,95,0,0 +2344,Cat,Siamese,24,Orange,Large,29.730664200070045,0,0,47,106,0,1 +2345,Cat,Persian,24,Orange,Medium,8.180895528066838,0,0,54,119,0,0 +2346,Cat,Siamese,4,White,Medium,15.687234934058907,1,0,46,100,0,1 +2347,Dog,Golden Retriever,67,Brown,Small,25.487667952525435,1,1,68,102,0,1 +2348,Bird,Parakeet,34,Gray,Large,7.937473196309881,0,0,4,165,0,1 +2349,Dog,Golden Retriever,177,Brown,Small,13.04119894384559,1,1,66,413,0,1 +2350,Dog,Poodle,116,Orange,Small,18.83764733372966,0,0,43,185,0,1 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Retriever,120,White,Small,21.105626587993267,1,0,53,231,1,1 +2492,Dog,Labrador,22,White,Small,23.75697904020139,1,0,6,183,1,1 +2493,Bird,Parakeet,115,Orange,Small,24.967155921194422,1,0,37,358,1,1 +2494,Bird,Parakeet,57,White,Large,27.49918509581016,1,0,2,274,0,0 +2495,Rabbit,Rabbit,72,Black,Medium,26.517957345512833,0,0,69,429,1,1 +2496,Rabbit,Rabbit,7,Orange,Medium,29.992496895484685,1,1,41,398,0,0 +2497,Cat,Siamese,119,White,Medium,8.077856318050287,1,0,51,31,0,0 +2498,Rabbit,Rabbit,114,White,Small,23.135920841154107,1,1,47,176,1,0 +2499,Cat,Persian,16,Gray,Medium,18.00732585886913,0,0,13,404,0,1 +2500,Cat,Persian,83,White,Large,8.57610884115389,1,0,76,64,1,0 +2501,Bird,Parakeet,179,Brown,Small,29.475254346321343,1,0,69,197,0,1 +2502,Dog,Poodle,72,Orange,Small,27.039045038453313,1,0,66,26,1,1 +2503,Rabbit,Rabbit,124,Brown,Small,4.7269543848793765,1,1,59,150,0,0 +2504,Rabbit,Rabbit,113,Orange,Small,1.7585919273791757,1,0,68,302,0,0 +2505,Dog,Labrador,12,Gray,Large,20.96159210764763,1,0,59,478,0,0 +2506,Rabbit,Rabbit,126,White,Medium,18.519788236839517,1,0,10,267,1,0 diff --git a/Pet Adoption Status/Model/Pet_Adoption_Status_Prediction.ipynb b/Pet Adoption Status/Model/Pet_Adoption_Status_Prediction.ipynb new file mode 100644 index 000000000..27e77847f --- /dev/null +++ b/Pet Adoption Status/Model/Pet_Adoption_Status_Prediction.ipynb @@ -0,0 +1,959 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "**Load the dataset and perform EDA**" + ], + "metadata": { + "id": "U6eZo_51oW4Y" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "vUsw2E5Ik7Be" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "source": [ + "# Load the dataset\n", + "data = pd.read_csv('/content/pet_adoption_data.csv')" + ], + "metadata": { + "id": "giiHIwE_optB" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Display the first few rows of the dataset\n", + "print(data.head())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bLx_03Dvo0Me", + "outputId": "19f19543-f9d5-4a69-c692-bb93aca149fa" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " PetID PetType Breed AgeMonths Color Size WeightKg \\\n", + "0 500 Bird Parakeet 131 Orange Large 5.039768 \n", + "1 501 Rabbit Rabbit 73 White Large 16.086727 \n", + "2 502 Dog Golden Retriever 136 Orange Medium 2.076286 \n", + "3 503 Bird Parakeet 97 White Small 3.339423 \n", + "4 504 Rabbit Rabbit 123 Gray Large 20.498100 \n", + "\n", + " Vaccinated HealthCondition TimeInShelterDays AdoptionFee PreviousOwner \\\n", + "0 1 0 27 140 0 \n", + "1 0 0 8 235 0 \n", + "2 0 0 85 385 0 \n", + "3 0 0 61 217 1 \n", + "4 0 0 28 14 1 \n", + "\n", + " AdoptionLikelihood \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Basic information about the dataset\n", + "print(data.info())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cPKA9hVgo5wH", + "outputId": "0d37f2fa-6821-497c-f352-b8260d868d3c" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 2007 entries, 0 to 2006\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PetID 2007 non-null int64 \n", + " 1 PetType 2007 non-null object \n", + " 2 Breed 2007 non-null object \n", + " 3 AgeMonths 2007 non-null int64 \n", + " 4 Color 2007 non-null object \n", + " 5 Size 2007 non-null object \n", + " 6 WeightKg 2007 non-null float64\n", + " 7 Vaccinated 2007 non-null int64 \n", + " 8 HealthCondition 2007 non-null int64 \n", + " 9 TimeInShelterDays 2007 non-null int64 \n", + " 10 AdoptionFee 2007 non-null int64 \n", + " 11 PreviousOwner 2007 non-null int64 \n", + " 12 AdoptionLikelihood 2007 non-null int64 \n", + "dtypes: float64(1), int64(8), object(4)\n", + "memory usage: 204.0+ KB\n", + "None\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Statistical summary of the dataset\n", + "print(data.describe())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RfRIdOdIo7jn", + "outputId": "e941f7cc-1f09-44f2-ffb2-f894c95e3911" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " PetID AgeMonths WeightKg Vaccinated HealthCondition \\\n", + "count 2007.000000 2007.000000 2007.000000 2007.000000 2007.000000 \n", + "mean 1503.000000 92.279522 15.705776 0.701046 0.196313 \n", + "std 579.515315 52.148363 8.327749 0.457914 0.397307 \n", + "min 500.000000 1.000000 1.018198 0.000000 0.000000 \n", + "25% 1001.500000 48.000000 8.730396 0.000000 0.000000 \n", + "50% 1503.000000 94.000000 15.925416 1.000000 0.000000 \n", + "75% 2004.500000 138.000000 22.737180 1.000000 0.000000 \n", + "max 2506.000000 179.000000 29.995628 1.000000 1.000000 \n", + "\n", + " TimeInShelterDays AdoptionFee PreviousOwner AdoptionLikelihood \n", + "count 2007.000000 2007.000000 2007.000000 2007.000000 \n", + "mean 43.974091 249.142003 0.301943 0.328351 \n", + "std 25.740253 142.887040 0.459215 0.469730 \n", + "min 1.000000 0.000000 0.000000 0.000000 \n", + "25% 21.000000 127.000000 0.000000 0.000000 \n", + "50% 45.000000 242.000000 0.000000 0.000000 \n", + "75% 66.000000 375.000000 1.000000 1.000000 \n", + "max 89.000000 499.000000 1.000000 1.000000 \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Checking for missing values\n", + "print(data.isnull().sum())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7lgvxM6co-D8", + "outputId": "9762cb74-d69e-404f-fe58-c9fdd9bb2b2d" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "PetID 0\n", + "PetType 0\n", + "Breed 0\n", + "AgeMonths 0\n", + "Color 0\n", + "Size 0\n", + "WeightKg 0\n", + "Vaccinated 0\n", + "HealthCondition 0\n", + "TimeInShelterDays 0\n", + "AdoptionFee 0\n", + "PreviousOwner 0\n", + "AdoptionLikelihood 0\n", + "dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Visualize the distribution of target variable\n", + "sns.countplot(data['AdoptionLikelihood'])\n", + "plt.title('Distribution of Adoption Status')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "a5BwRBg4pAuS", + "outputId": "e99c7dbd-702f-4f3a-a7df-1492da2925c3" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Handle non-numerical columns before calculating correlations\n", + "# Select only numerical columns or convert relevant columns to numerical types\n", + "numerical_data = data.select_dtypes(include=['number'])\n", + "\n", + "# Visualize correlation matrix\n", + "corr_matrix = numerical_data.corr()\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n", + "plt.title('Correlation Matrix')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "frpxqgdDpZUS", + "outputId": "e5d2237e-610f-40cd-b428-e5ebd476c57a" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Data Preprocessing and Splitting Data**" + ], + "metadata": { + "id": "zTL5O-YopmbC" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import LabelEncoder" + ], + "metadata": { + "id": "pPY6gPsCpooa" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Handle categorical variables\n", + "categorical_cols = data.select_dtypes(include=['object']).columns\n", + "label_encoders = {}\n", + "\n", + "for col in categorical_cols:\n", + " label_encoders[col] = LabelEncoder()\n", + " data[col] = label_encoders[col].fit_transform(data[col])" + ], + "metadata": { + "id": "zMrACjbHprk_" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Handle missing values (if any)\n", + "data.fillna(data.mean(), inplace=True)" + ], + "metadata": { + "id": "WBC5jz7ppvKW" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Define features and target\n", + "X = data.drop('AdoptionLikelihood', axis=1)\n", + "y = data['AdoptionLikelihood']" + ], + "metadata": { + "id": "W7IGeeztpyuA" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Splitting the dataset into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ], + "metadata": { + "id": "G7ITv9_Pp41Y" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)" + ], + "metadata": { + "id": "NC7NAidDp9MN" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model Training, Evaluation, and Prediction**" + ], + "metadata": { + "id": "eVfvsE-iqCeO" + } + }, + { + "cell_type": "markdown", + "source": [ + "**LSTM**" + ], + "metadata": { + "id": "HDL2zkBEqGnc" + } + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report" + ], + "metadata": { + "id": "CDm_lueAqJZ1" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Reshape data for LSTM model\n", + "X_train_lstm = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])\n", + "X_test_lstm = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])" + ], + "metadata": { + "id": "gW3XVaURqPBr" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# LSTM model\n", + "model_lstm = Sequential()\n", + "model_lstm.add(LSTM(50, return_sequences=True, input_shape=(1, X_train.shape[1])))\n", + "model_lstm.add(Dropout(0.2))\n", + "model_lstm.add(LSTM(50, return_sequences=False))\n", + "model_lstm.add(Dropout(0.2))\n", + "model_lstm.add(Dense(1, activation='sigmoid'))" + ], + "metadata": { + "id": "T8CUjZu2qR1-" + }, + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model_lstm.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", + "model_lstm.fit(X_train_lstm, y_train, epochs=20, batch_size=32, validation_split=0.2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tkQ2Q27cqTxZ", + "outputId": "9bdfcae0-c8dc-4e4a-ccb2-b2caf26d4c9e" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/20\n", + "41/41 [==============================] - 9s 32ms/step - loss: 0.6723 - accuracy: 0.6737 - val_loss: 0.6447 - val_accuracy: 0.6978\n", + "Epoch 2/20\n", + "41/41 [==============================] - 0s 6ms/step - loss: 0.6048 - accuracy: 0.7118 - val_loss: 0.5514 - val_accuracy: 0.7227\n", + "Epoch 3/20\n", + "41/41 [==============================] - 0s 6ms/step - loss: 0.5226 - accuracy: 0.7391 - val_loss: 0.4942 - val_accuracy: 0.7477\n", + "Epoch 4/20\n", + "41/41 [==============================] - 0s 7ms/step - loss: 0.4970 - accuracy: 0.7547 - val_loss: 0.4796 - val_accuracy: 0.7601\n", + "Epoch 5/20\n", + "41/41 [==============================] - 0s 9ms/step - loss: 0.4906 - accuracy: 0.7375 - val_loss: 0.4715 - val_accuracy: 0.7850\n", + "Epoch 6/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.4807 - accuracy: 0.7570 - val_loss: 0.4680 - val_accuracy: 0.7850\n", + "Epoch 7/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.4675 - accuracy: 0.7625 - val_loss: 0.4572 - val_accuracy: 0.7882\n", + "Epoch 8/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.4614 - accuracy: 0.7617 - val_loss: 0.4447 - val_accuracy: 0.7913\n", + "Epoch 9/20\n", + "41/41 [==============================] - 0s 7ms/step - loss: 0.4476 - accuracy: 0.7780 - val_loss: 0.4285 - val_accuracy: 0.8193\n", + "Epoch 10/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.4270 - accuracy: 0.8053 - val_loss: 0.4087 - val_accuracy: 0.8380\n", + "Epoch 11/20\n", + "41/41 [==============================] - 0s 9ms/step - loss: 0.4084 - accuracy: 0.8115 - val_loss: 0.3871 - val_accuracy: 0.8567\n", + "Epoch 12/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.3752 - accuracy: 0.8279 - val_loss: 0.3672 - val_accuracy: 0.8723\n", + "Epoch 13/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.3543 - accuracy: 0.8505 - val_loss: 0.3468 - val_accuracy: 0.8816\n", + "Epoch 14/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.3334 - accuracy: 0.8583 - val_loss: 0.3293 - val_accuracy: 0.8910\n", + "Epoch 15/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.3152 - accuracy: 0.8723 - val_loss: 0.3203 - val_accuracy: 0.8816\n", + "Epoch 16/20\n", + "41/41 [==============================] - 0s 7ms/step - loss: 0.3018 - accuracy: 0.8801 - val_loss: 0.3120 - val_accuracy: 0.8879\n", + "Epoch 17/20\n", + "41/41 [==============================] - 0s 9ms/step - loss: 0.3003 - accuracy: 0.8723 - val_loss: 0.3083 - val_accuracy: 0.8847\n", + "Epoch 18/20\n", + "41/41 [==============================] - 0s 7ms/step - loss: 0.2862 - accuracy: 0.8863 - val_loss: 0.3037 - val_accuracy: 0.8785\n", + "Epoch 19/20\n", + "41/41 [==============================] - 0s 7ms/step - loss: 0.2784 - accuracy: 0.8832 - val_loss: 0.3027 - val_accuracy: 0.8816\n", + "Epoch 20/20\n", + "41/41 [==============================] - 0s 8ms/step - loss: 0.2720 - accuracy: 0.8902 - val_loss: 0.2984 - val_accuracy: 0.8941\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Predictions\n", + "y_pred_lstm = (model_lstm.predict(X_test_lstm) > 0.5).astype(\"int32\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TrhOD890qXTC", + "outputId": "0e3493f2-6399-485d-dbd2-69202ed78a8f" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "13/13 [==============================] - 2s 8ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Evaluation\n", + "print(\"LSTM Model Accuracy:\", accuracy_score(y_test, y_pred_lstm))\n", + "print(confusion_matrix(y_test, y_pred_lstm))\n", + "print(classification_report(y_test, y_pred_lstm))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2kfnlQ73qiKY", + "outputId": "822352ba-d08e-44ec-8d32-0b23d948c30b" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "LSTM Model Accuracy: 0.8781094527363185\n", + "[[247 23]\n", + " [ 26 106]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.91 0.91 270\n", + " 1 0.82 0.80 0.81 132\n", + "\n", + " accuracy 0.88 402\n", + " macro avg 0.86 0.86 0.86 402\n", + "weighted avg 0.88 0.88 0.88 402\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**KNN**" + ], + "metadata": { + "id": "agu99B18qn6L" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.neighbors import KNeighborsClassifier" + ], + "metadata": { + "id": "OoZ5pmplqqKS" + }, + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# KNN model\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn.fit(X_train, y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "id": "5EMHd0toqsWc", + "outputId": "bf930c3d-f65e-491b-d15e-ff694fc178e4" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "KNeighborsClassifier()" + ], + "text/html": [ + "
KNeighborsClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Predictions\n", + "y_pred_knn = knn.predict(X_test)" + ], + "metadata": { + "id": "j74P0Pmnqwde" + }, + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Evaluation\n", + "print(\"KNN Model Accuracy:\", accuracy_score(y_test, y_pred_knn))\n", + "print(confusion_matrix(y_test, y_pred_knn))\n", + "print(classification_report(y_test, y_pred_knn))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "axonldh2q0lc", + "outputId": "16843509-6da0-41d8-de85-9b0a216f4865" + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "KNN Model Accuracy: 0.7587064676616916\n", + "[[221 49]\n", + " [ 48 84]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.82 0.82 270\n", + " 1 0.63 0.64 0.63 132\n", + "\n", + " accuracy 0.76 402\n", + " macro avg 0.73 0.73 0.73 402\n", + "weighted avg 0.76 0.76 0.76 402\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**K-Means Clustering**" + ], + "metadata": { + "id": "nQFspQ5cq4Pa" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score" + ], + "metadata": { + "id": "Gvj1yoAMq6rE" + }, + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# K-Means Clustering\n", + "kmeans = KMeans(n_clusters=2, random_state=42)\n", + "kmeans.fit(X_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 129 + }, + "id": "T66uqDX1q8iB", + "outputId": "e6c811ae-8931-447e-d4cb-86c7dcd19566" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "KMeans(n_clusters=2, random_state=42)" + ], + "text/html": [ + "
KMeans(n_clusters=2, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 31 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Cluster labels for training set\n", + "train_clusters = kmeans.predict(X_train)\n", + "test_clusters = kmeans.predict(X_test)" + ], + "metadata": { + "id": "FpesdOVFq-s9" + }, + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Evaluation\n", + "print(\"K-Means Silhouette Score:\", silhouette_score(X_test, test_clusters))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JhNA-15CrBIs", + "outputId": "762c91d4-5f4b-43a2-f3e2-1f6811735edb" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "K-Means Silhouette Score: 0.09654303048720678\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**XGBoost**" + ], + "metadata": { + "id": "A6NKbTv9rFrS" + } + }, + { + "cell_type": "code", + "source": [ + "import xgboost as xgb\n", + "from xgboost import XGBClassifier" + ], + "metadata": { + "id": "qUdZ7xBNrD0Q" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# XGBoost model\n", + "xgb_model = XGBClassifier()\n", + "xgb_model.fit(X_train, y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 248 + }, + "id": "dOELlOHLrLVZ", + "outputId": "dadbc692-cc43-45c6-e21a-6ca3636ff17f" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)" + ], + "text/html": [ + "
XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "              gamma=None, grow_policy=None, importance_type=None,\n",
+              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Predictions\n", + "y_pred_xgb = xgb_model.predict(X_test)" + ], + "metadata": { + "id": "96duaGpvrNJJ" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Evaluation\n", + "print(\"XGBoost Model Accuracy:\", accuracy_score(y_test, y_pred_xgb))\n", + "print(confusion_matrix(y_test, y_pred_xgb))\n", + "print(classification_report(y_test, y_pred_xgb))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WHAftJv1rQEG", + "outputId": "0455bb1d-1d04-45b3-9183-a88dd9a76750" + }, + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "XGBoost Model Accuracy: 0.9502487562189055\n", + "[[259 11]\n", + " [ 9 123]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.97 0.96 0.96 270\n", + " 1 0.92 0.93 0.92 132\n", + "\n", + " accuracy 0.95 402\n", + " macro avg 0.94 0.95 0.94 402\n", + "weighted avg 0.95 0.95 0.95 402\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Visualize Results**" + ], + "metadata": { + "id": "qkd8G_uerSqv" + } + }, + { + "cell_type": "code", + "source": [ + "# Function to plot stock analysis chart (for demonstration purposes, using feature importance in this case)\n", + "def plot_feature_importance(model, X):\n", + " feature_importance = model.feature_importances_\n", + " sorted_idx = np.argsort(feature_importance)\n", + " pos = np.arange(sorted_idx.shape[0]) + 0.5\n", + " plt.figure(figsize=(12, 8))\n", + " plt.barh(pos, feature_importance[sorted_idx], align='center')\n", + " plt.yticks(pos, np.array(X.columns)[sorted_idx])\n", + " plt.title('Feature Importance')\n", + " plt.show()" + ], + "metadata": { + "id": "TKsDEXkNrVBt" + }, + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Plot feature importance for XGBoost\n", + "plot_feature_importance(xgb_model, X)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "id": "81SGyxjuraKf", + "outputId": "0a68008e-34a4-4a3e-e6b1-ed1238a91d5f" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/Pet Adoption Status/requirements.txt b/Pet Adoption Status/requirements.txt new file mode 100644 index 000000000..f4710ca53 --- /dev/null +++ b/Pet Adoption Status/requirements.txt @@ -0,0 +1,5 @@ +**Requirements For Project :-** + +1. NumPy: Fundamental package for numerical computing. +2. pandas: Data analysis and manipulation library. +3. scikit-learn: Machine learning library for classification, regression, and clustering. \ No newline at end of file